<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100000 entries, 0 to 99999
Data columns (total 40 columns):
 #   Column                        Non-Null Count   Dtype  
---  ------                        --------------   -----  
 0   Student_ID                    100000 non-null  object 
 1   Name                          100000 non-null  object 
 2   Gender                        100000 non-null  object 
 3   Age                           95226 non-null   float64
 4   Education_Level               100000 non-null  object 
 5   Employment_Status             100000 non-null  object 
 6   City                          100000 non-null  object 
 7   Device_Type                   100000 non-null  object 
 8   Internet_Connection_Quality   100000 non-null  object 
 9   Course_ID                     100000 non-null  object 
 10  Course_Name                   100000 non-null  object 
 11  Category                      100000 non-null  object 
 12  Course_Level                  100000 non-null  object 
 13  Course_Duration_Days          100000 non-null  int64  
 14  Instructor_Rating             100000 non-null  float64
 15  Login_Frequency               99888 non-null   float64
 16  Average_Session_Duration_Min  100000 non-null  int64  
 17  Video_Completion_Rate         100000 non-null  float64
 18  Discussion_Participation      99045 non-null   float64
 19  Time_Spent_Hours              100000 non-null  float64
 20  Days_Since_Last_Login         100000 non-null  int64  
 21  Notifications_Checked         100000 non-null  int64  
 22  Peer_Interaction_Score        100000 non-null  float64
 23  Assignments_Submitted         99048 non-null   float64
 24  Assignments_Missed            100000 non-null  int64  
 25  Quiz_Attempts                 100000 non-null  int64  
 26  Quiz_Score_Avg                100000 non-null  float64
 27  Project_Grade                 100000 non-null  float64
 28  Progress_Percentage           99665 non-null   float64
 29  Rewatch_Count                 97524 non-null   float64
 30  Enrollment_Date               100000 non-null  object 
 31  Payment_Mode                  100000 non-null  object 
 32  Fee_Paid                      100000 non-null  object 
 33  Discount_Used                 100000 non-null  object 
 34  Payment_Amount                100000 non-null  int64  
 35  App_Usage_Percentage          99974 non-null   float64
 36  Reminder_Emails_Clicked       100000 non-null  int64  
 37  Support_Tickets_Raised        100000 non-null  int64  
 38  Satisfaction_Rating           98918 non-null   float64
 39  Completed                     100000 non-null  object 
dtypes: float64(14), int64(9), object(17)
memory usage: 30.5+ MB

Student_ID
['STU100000' 'STU100001' 'STU100002' ... 'STU199997' 'STU199998'
 'STU199999']

Number of unique values in Student_ID are:100000

----------------------------------------------------------------------------------
Name
['Vihaan Patel' 'Arjun Nair' 'Aditya Bhardwaj' 'Krishna Singh'
 'Krishna Nair' 'Rohan Reddy' 'Sai Nair' 'Krishna Desai' 'Vihaan Joshi'
 'Vivaan Nair' 'Aditya Gupta' 'Sneha Bhardwaj' 'Rohan Desai'
 'Vihaan Mehta' 'Sai Joshi' 'Sai Reddy' 'Rahul Singh' 'Rohan Shah'
 'Neha Sharma' 'Pooja Singh' 'Kavya Verma' 'Rahul Kumar' 'Priya Joshi'
 'Ritika Reddy' 'Arjun Iyer' 'Sakshi Sharma' 'Ritika Kumar' 'Vihaan Iyer'
 'Pooja Sharma' 'Aarav Nair' 'Sai Gupta' 'Ananya Shah' 'Arjun Joshi'
 'Aarav Bhardwaj' 'Sneha Bose' 'Vivaan Singh' 'Aditya Nair' 'Arjun Verma'
 'Sai Kumar' 'Krishna Bose' 'Sneha Reddy' 'Rohan Singh' 'Krishna Gupta'
 'Nikhil Reddy' 'Krishna Sharma' 'Isha Mehta' 'Isha Verma' 'Kavya Desai'
 'Vivaan Iyer' 'Rohan Verma' 'Priya Bose' 'Priya Kumar' 'Ananya Mehta'
 'Meera Verma' 'Nikhil Nair' 'Vivaan Patel' 'Sai Patel' 'Priya Sharma'
 'Sakshi Patel' 'Nikhil Bhardwaj' 'Krishna Iyer' 'Priya Verma'
 'Pooja Bose' 'Kavya Bhardwaj' 'Aditya Bose' 'Kavya Shah' 'Ritika Patel'
 'Vihaan Bose' 'Rahul Verma' 'Aditya Iyer' 'Aarav Joshi' 'Rahul Patel'
 'Arjun Gupta' 'Sneha Singh' 'Sneha Desai' 'Sakshi Nair' 'Sai Desai'
 'Aarav Verma' 'Rohan Kumar' 'Priya Shah' 'Krishna Shah' 'Pooja Kumar'
 'Sneha Iyer' 'Pooja Patel' 'Sai Bhardwaj' 'Isha Joshi' 'Sakshi Gupta'
 'Krishna Kumar' 'Ritika Bose' 'Krishna Joshi' 'Pooja Bhardwaj'
 'Ananya Gupta' 'Priya Nair' 'Isha Iyer' 'Sakshi Bhardwaj' 'Ananya Kumar'
 'Sakshi Iyer' 'Aditya Kumar' 'Ananya Singh' 'Kavya Mehta' 'Priya Reddy'
 'Aarav Bose' 'Isha Singh' 'Rahul Bhardwaj' 'Sakshi Shah' 'Rahul Reddy'
 'Kavya Kumar' 'Neha Desai' 'Aditya Verma' 'Pooja Gupta' 'Aarav Shah'
 'Meera Patel' 'Isha Gupta' 'Aarav Mehta' 'Sai Shah' 'Aarav Sharma'
 'Kavya Joshi' 'Rohan Iyer' 'Vivaan Desai' 'Vihaan Bhardwaj' 'Sneha Verma'
 'Nikhil Iyer' 'Rohan Sharma' 'Sakshi Desai' 'Vivaan Joshi' 'Meera Sharma'
 'Kavya Nair' 'Neha Bhardwaj' 'Pooja Shah' 'Neha Bose' 'Meera Shah'
 'Neha Gupta' 'Aditya Singh' 'Rohan Nair' 'Rahul Joshi' 'Sai Singh'
 'Nikhil Desai' 'Vivaan Gupta' 'Meera Reddy' 'Neha Shah' 'Vivaan Verma'
 'Isha Kumar' 'Vihaan Desai' 'Meera Kumar' 'Kavya Sharma' 'Vihaan Gupta'
 'Arjun Sharma' 'Sakshi Kumar' 'Meera Gupta' 'Aditya Reddy' 'Vihaan Shah'
 'Vivaan Reddy' 'Kavya Bose' 'Vihaan Reddy' 'Meera Bose' 'Isha Shah'
 'Kavya Gupta' 'Ritika Nair' 'Rahul Mehta' 'Rohan Bhardwaj' 'Priya Iyer'
 'Meera Desai' 'Sneha Mehta' 'Kavya Iyer' 'Nikhil Kumar' 'Neha Reddy'
 'Vivaan Bose' 'Krishna Bhardwaj' 'Vivaan Kumar' 'Sneha Nair' 'Pooja Nair'
 'Rahul Bose' 'Ananya Nair' 'Priya Patel' 'Vihaan Verma' 'Arjun Mehta'
 'Kavya Reddy' 'Sneha Gupta' 'Vivaan Mehta' 'Arjun Desai' 'Pooja Desai'
 'Aarav Kumar' 'Nikhil Joshi' 'Aditya Joshi' 'Priya Bhardwaj'
 'Sakshi Mehta' 'Rahul Nair' 'Rohan Mehta' 'Isha Nair' 'Neha Joshi'
 'Aditya Sharma' 'Vihaan Nair' 'Pooja Iyer' 'Krishna Verma' 'Sneha Shah'
 'Nikhil Singh' 'Sakshi Bose' 'Pooja Joshi' 'Isha Reddy' 'Sakshi Reddy'
 'Ananya Joshi' 'Sai Verma' 'Ritika Gupta' 'Aditya Mehta' 'Aarav Reddy'
 'Nikhil Mehta' 'Sneha Sharma' 'Isha Bhardwaj' 'Arjun Singh'
 'Sakshi Joshi' 'Arjun Reddy' 'Ananya Desai' 'Ritika Sharma'
 'Nikhil Gupta' 'Sneha Joshi' 'Arjun Bose' 'Sneha Patel' 'Ritika Desai'
 'Arjun Bhardwaj' 'Rohan Gupta' 'Ritika Mehta' 'Arjun Shah' 'Vihaan Kumar'
 'Meera Nair' 'Neha Singh' 'Ananya Sharma' 'Rahul Gupta' 'Arjun Patel'
 'Meera Iyer' 'Kavya Patel' 'Pooja Verma' 'Sakshi Verma' 'Ritika Joshi'
 'Neha Nair' 'Isha Desai' 'Nikhil Verma' 'Ritika Verma' 'Rohan Joshi'
 'Ritika Iyer' 'Aditya Patel' 'Ananya Reddy' 'Rahul Sharma'
 'Meera Bhardwaj' 'Priya Gupta' 'Pooja Mehta' 'Pooja Reddy' 'Ananya Patel'
 'Neha Verma' 'Aarav Gupta' 'Sai Mehta' 'Priya Desai' 'Neha Iyer'
 'Sakshi Singh' 'Ritika Singh' 'Aarav Desai' 'Aarav Singh' 'Meera Singh'
 'Vivaan Shah' 'Ananya Bose' 'Rahul Iyer' 'Vivaan Bhardwaj' 'Sai Sharma'
 'Ananya Iyer' 'Vivaan Sharma' 'Nikhil Shah' 'Sai Bose' 'Aditya Desai'
 'Krishna Reddy' 'Nikhil Sharma' 'Vihaan Singh' 'Meera Mehta' 'Rahul Shah'
 'Ananya Bhardwaj' 'Ananya Verma' 'Vihaan Sharma' 'Krishna Patel'
 'Rahul Desai' 'Isha Patel' 'Nikhil Patel' 'Ritika Bhardwaj' 'Neha Mehta'
 'Meera Joshi' 'Isha Bose' 'Priya Singh' 'Rohan Bose' 'Ritika Shah'
 'Aarav Iyer' 'Krishna Mehta' 'Rohan Patel' 'Priya Mehta' 'Sai Iyer'
 'Aarav Patel' 'Arjun Kumar' 'Sneha Kumar' 'Neha Kumar' 'Aditya Shah'
 'Kavya Singh' 'Nikhil Bose' 'Neha Patel' 'Isha Sharma']

Number of unique values in Name are:300

----------------------------------------------------------------------------------
Gender
['Male' 'Female' 'Other']

Number of unique values in Gender are:3

----------------------------------------------------------------------------------
Age
[19. 17. 34. 29. 21. 22. 23. nan 31. 28. 24. 26. 27. 25. 35. 18. 33. 20.
 30. 36. 41. 38. 39. 43. 40. 42. 44. 46. 45. 48. 49. 52.]

Number of unique values in Age are:32

----------------------------------------------------------------------------------
Education_Level
['Diploma' 'Bachelor' 'Master' 'HighSchool' 'PhD']

Number of unique values in Education_Level are:5

----------------------------------------------------------------------------------
Employment_Status
['Student' 'Employed' 'Self-Employed' 'Unemployed']

Number of unique values in Employment_Status are:4

----------------------------------------------------------------------------------
City
['Indore' 'Delhi' 'Chennai' 'Surat' 'Lucknow' 'Jaipur' 'Hyderabad'
 'Nagpur' 'Kolkata' 'Ahmedabad' 'Pune' 'Mumbai' 'Bengaluru' 'Bhopal'
 'Vadodara']

Number of unique values in City are:15

----------------------------------------------------------------------------------
Device_Type
['Laptop' 'Mobile' 'Tablet']

Number of unique values in Device_Type are:3

----------------------------------------------------------------------------------
Internet_Connection_Quality
['Medium' 'Low' 'High']

Number of unique values in Internet_Connection_Quality are:3

----------------------------------------------------------------------------------
Course_ID
['C102' 'C106' 'C101' 'C105' 'C103' 'C104' 'C107' 'C108']

Number of unique values in Course_ID are:8

----------------------------------------------------------------------------------
Course_Name
['Data Analysis with Python' 'Machine Learning A-Z' 'Python Basics'
 'UI/UX Design Fundamentals' 'Introduction to AI'
 'Digital Marketing Essentials' 'Statistics for Data Science'
 'Excel for Business']

Number of unique values in Course_Name are:8

----------------------------------------------------------------------------------
Category
['Programming' 'Design' 'Marketing' 'Math' 'Business']

Number of unique values in Category are:5

----------------------------------------------------------------------------------
Course_Level
['Intermediate' 'Advanced' 'Beginner']

Number of unique values in Course_Level are:3

----------------------------------------------------------------------------------
Course_Duration_Days
[60 90 45 40 75 30 50 25]

Number of unique values in Course_Duration_Days are:8

----------------------------------------------------------------------------------
Instructor_Rating
[4.7 4.6 4.4 4.5 4.3 4.2 4.1]

Number of unique values in Instructor_Rating are:7

----------------------------------------------------------------------------------
Login_Frequency
[ 3.  4.  5.  2.  7.  8.  6.  9.  1. 10. 11. nan  0. 14. 15.]

Number of unique values in Login_Frequency are:15

----------------------------------------------------------------------------------
Average_Session_Duration_Min
[30 37  9 27 36 43 12 20 38 28 21 23 34 44 58 42 32 35 39 33 26 13 24 53
 49 50 25 17 40 62 51 16 31 46 41 45 29 48  8 19 22 14  5 18 47 52 61 59
 10 56 57 11 63 54 15  6 55 65 66  7 60 64 74 67 72 68 76 70 71 69 75 73
 81]

Number of unique values in Average_Session_Duration_Min are:73

----------------------------------------------------------------------------------
Video_Completion_Rate
[55.  84.1 75.6 63.3 86.4 85.9 94.5 79.2 82.3 63.4 82.9 64.3 53.5 88.2
 73.8 92.4 83.5 55.6 89.5 48.4 66.1 86.3 47.2 49.1 49.7 87.1 22.2 57.3
 53.3 23.6 95.4 60.2 55.2 70.4 51.8 64.4 57.2 69.8 63.1 64.2 24.2 65.6
 72.2 37.  61.6 48.1 29.3 54.7 40.7 80.4 85.6 47.8 61.9 85.5 71.9 88.3
 77.7 62.  76.  56.  38.3 57.1 71.2 56.7 94.2 84.8 75.4  5.6 51.2 28.6
 33.2 49.  81.3 83.1 49.9 96.4 98.1 44.  64.  66.4 77.6 39.1 54.6 93.6
 52.9 95.2 66.7 38.7 68.  85.  50.4 51.5 64.7 81.6 44.4 32.4 91.4 46.9
 40.6 43.9 86.9 49.4 85.1 24.5 38.  71.8 58.6 44.2 86.8 29.9 92.  90.2
 57.7 71.3 36.2 81.4 24.4 38.1 22.6 46.1 78.5 96.5 30.5 43.2 67.7 81.8
 80.8 81.5 62.1 84.7 61.5 97.7 16.  23.2 12.8 69.3 46.5 78.  33.5 72.5
 63.2 28.9 52.5 33.1 53.9 84.2 37.9 73.6 44.9 33.3 68.7 67.9 75.9 85.8
 60.  37.3 15.3 27.1 39.6 27.7 32.2 73.  47.  38.5 35.1 83.8 78.9 77.2
 76.3 94.4 66.  89.2 55.8 88.8 59.9 39.2 57.4 89.8 44.5 74.5 85.7 71.4
 70.8 67.1 64.5 52.1 52.  59.4 51.3 72.  76.8 80.5 74.6 70.7 83.4 82.5
 66.8 59.2 61.1 46.8 56.8 36.  92.8 47.4 38.6 25.6 61.7 88.  88.4 67.
 69.6 88.1 45.3 72.3 20.2 32.8 78.4 54.1 72.8 83.3 74.4 46.  51.6 89.3
 75.3 86.  91.3 34.2 45.  30.4 49.3 52.6 54.  70.3 80.7 67.8 71.6 92.7
 24.3 14.7 43.7 58.2 95.7 80.6 83.  45.8 46.2 68.9 51.9 70.6 54.3 76.6
 42.4 48.9 33.8 62.8 64.9 92.1 53.2 80.3 71.1 90.7 52.8 82.6 38.9 87.5
 76.2 75.5 50.3 64.8 77.4 93.  47.5 25.2 58.8 48.6 52.7 39.8 44.7 33.6
 68.3 57.9 66.5 76.5 90.  69.  94.9 53.8 58.3 87.8 34.6 55.9 65.5 35.7
 41.  58.7 42.8 30.1 50.7 25.  79.8 89.1 61.  58.9 53.7 78.1 82.1 72.7
 82.4 66.6 56.2 93.7 74.3 37.6 69.9 86.6 43.6 74.9 65.3 58.5 92.3 69.2
 84.6 79.  85.3 95.1 35.  73.4 54.9 50.  99.2 68.1 63.9 47.3 56.1 65.2
 71.  73.9 42.5 90.9 80.1 43.  29.8 45.9 28.  48.5 73.7 50.9 31.7 89.4
 45.1 31.3 26.5 67.3 97.3 42.6 42.  37.5 73.2 19.6 57.5 44.1 40.  59.5
 72.6 83.2 50.6 79.7 54.4 90.4 94.8 65.  87.6 26.7 96.7 53.4 81.7 52.2
 45.4 73.1 38.2 82.7 39.3 47.6 77.8 72.4 43.1 56.5 68.5 76.9 74.7 68.8
 75.8 89.9 44.6 65.9 26.1 58.1 95.5 55.4 61.4 70.  87.9 54.5 68.2 50.2
 68.4 45.7 56.3 97.6 48.7 60.8 59.  79.6 59.8 91.2 90.5 46.6 91.8 85.2
 74.1 73.5 18.9 53.  77.5 41.3 35.5 65.8 82.2 86.2 27.8 27.2 63.5 83.9
 54.2 34.8 56.9 31.6 78.3 62.2 65.1 43.3 26.8 30.2 55.5 10.6 19.  55.7
 50.5 26.6 14.  41.4 56.6 75.  96.2 11.7 21.6 52.4 62.5 19.8 57.8 78.7
 80.9 77.1 29.5 79.3 34.4 74.2 81.1 56.4 23.1 20.9 79.1 93.3 70.9 86.5
 77.3 15.7 94.1 88.5 25.3 76.7 22.1 75.1 36.4 81.  47.9 77.  94.6 37.8
 27.5 80.  72.9 61.3 20.4 88.6 27.6 32.5 82.8 41.7 59.1 59.3 59.6 69.4
 31.  84.3 41.1 61.2 16.1 28.5 31.8 33.9 68.6 62.4 45.5 84.9 31.2 92.6
 33.  36.5 91.  34.5 60.6 41.9 87.3 96.1 95.9 79.4 25.1 94.  46.7 35.9
 98.4 88.9 83.7 87.  26.9 76.4 77.9 60.5 67.6 71.7 18.  66.2 70.1 90.1
 70.2 60.3 39.7 63.6 66.9 75.7 24.7 43.5 52.3 73.3 46.4 12.  36.6 87.2
 17.6 65.7 41.2 32.9 44.8 24.6 51.4 62.7 35.4 34.9 93.1 96.9 43.4 20.5
 39.  19.7  6.8 37.2 67.2 75.2 18.1 89.6 99.5 50.8 49.5 69.5 72.1 91.1
 92.9 86.7 30.8 69.7 86.1 62.9 91.6 21.8 28.2 58.  60.1 29.1 62.3 79.9
 90.3 39.4 48.  89.7 22.7 97.4 19.4 40.8 40.3 41.6  9.5 32.  57.  98.9
 51.  40.2 71.5 42.3 15.4 35.3 78.2 80.2 21.4 49.6 58.4 12.9 30.3 21.1
 41.8 60.7 15.  51.1 95.8 19.1 64.6 78.6 42.7 32.3 34.1 66.3 49.8 22.3
 70.5 17.4 32.7 36.8 33.7 48.8 31.5 84.4 98.5 88.7 24.9 37.1 53.6 32.6
 93.2 23.5 54.8 95.3 23.8 48.3 63.7 93.9 78.8 34.7 87.4 40.1 74.  87.7
 57.6 67.4 60.9 91.7 43.8 98.2 23.7 28.4 23.3 85.4 98.8 81.2 60.4 63.8
 42.9 51.7 37.4 31.9 89.  36.7 97.  16.8 65.4 63.  18.5 21.  42.2 26.3
 24.8 79.5 76.1 55.1 22.8 34.  45.6 49.2 47.1 23.9 96.  34.3 38.8 36.3
 33.4 95.  16.2  8.3 62.6  7.3 91.5 69.1 99.  12.3 39.9 93.5 96.6 35.6
 93.8 12.2 64.1 30.  37.7 82.  40.5 20.8 50.1 44.3 12.1 84.5 92.2 45.2
 74.8 31.4 35.8 20.6 67.5 25.4 46.3  8.5 16.4 20.  91.9 12.4 98.  61.8
 30.6 42.1 97.2 17.3 97.1 17.7 22.  35.2 22.5 29.7 41.5 81.9 11.4 32.1
 38.4 20.3 14.2 19.2 36.9 84.   7.8 24.  40.4 14.1 53.1 14.3  8.4 90.6
 16.5 28.1 18.2 21.2 39.5 28.3 29.4 11.1 48.2 28.8 20.7 59.7 26.2 17.5
 99.7 31.1 29.   7.6 27.9 98.3  5.  17.8 95.6 26.  36.1 99.6 26.4 17.1
 25.8 92.5 40.9 25.9 12.7 94.3 18.4 94.7 90.8 27.  27.3 17.  17.2  8.6
 22.9 93.4 15.2 98.6 13.5 15.8 55.3 17.9 96.8 83.6 13.8 30.9 21.3 97.9
 18.3 27.4  5.2 47.7 14.4 15.1 23.  16.6 28.7 25.5 16.3 29.2 21.9 30.7
 13.1 21.5 96.3  9.1 29.6  9.3 10.9  6.3  7.4 13.3 11.8 14.9 18.6 10.7
 23.4 20.1 13.2 99.1 10.8 98.7 15.6 22.4 10.4 18.8 13.6 10.  14.5  8.8
 18.7  6.6 25.7 19.9 21.7  7.  11.9 99.9 99.3 19.5 13.  24.1 15.5 16.7
 14.8 14.6 15.9  6.9 97.5 19.3 13.4  8.7 12.5 11.5 13.7 11.6  7.7  9.
 12.6  9.9  5.4  9.7 16.9 10.3  6.4 11.3 10.1 97.8  7.5 11.2  8.1  8.9
  9.8 10.2  5.9 99.8 10.5  6.7  7.1  9.4 13.9  5.5 99.4  5.8  9.2  9.6
  8.   8.2  6.5  7.9  5.1  6.   5.3  6.2 11.   5.7  6.1  7.2]

Number of unique values in Video_Completion_Rate are:950

----------------------------------------------------------------------------------
Discussion_Participation
[ 2.  3.  1.  5.  4.  0.  6.  8. nan  9. 12. 11.]

Number of unique values in Discussion_Participation are:12

----------------------------------------------------------------------------------
Time_Spent_Hours
[ 0.5  0.9  7.4 11.8  3.3  4.2  2.9 13.4  9.2 10.5  3.5  9.8  7.2  6.3
  4.3  1.1  3.7  2.3  6.5 12.6  8.   1.   1.8 18.8  2.4  2.8 10.9  4.4
  9.5  9.6  2.2  7.5  7.6  1.3  7.   3.2  0.6  9.3  3.6  7.7 12.3  4.1
 13.6  1.9  0.8  8.8  8.2  3.9  8.5  2.1  1.4 11.3 10.6  3.4  4.9  6.8
  5.4  8.3  8.4  4.8  2.7  6.6  8.7 10.3  5.7  5.9  6.7  4.  15.6  1.2
  4.6  1.6  9.7 11.7  2.5  2.   6.   5.8  3.8  1.5  6.1  5.  12.  13.5
  6.4 12.4  3.1 16.5  5.6  6.9 10.8  5.3  5.5  4.7 10.1 13.9 15.9  1.7
  3.   4.5 11.6 10.2  5.2  5.1  8.6  7.8 11.5 13.2 14.4 12.2  8.9  8.1
 12.9  6.2  7.1 11.  10.   9.4 16.8 13.7 13.  15.4  9.9  2.6 11.1 11.9
  0.7  7.9 13.8 13.1 10.7 17.5  9.  15.  15.3  7.3 10.4  9.1 15.7 16.4
 12.1 12.5 11.4 18.3 16.6 14.5 14.8 14.2 14.1 14.9 12.7 13.3 15.5 21.8
 12.8 15.2 14.7 14.6 19.5 17.6 17.1 21.2 14.3 11.2 17.4 16.  17.7 16.3
 14.  17.8 19.3 15.8 16.1 20.1 17.3 17.  18.1 15.1 20.3 22.7 16.2 18.4
 16.9 23.4 16.7 18.6 19.7 17.2 18.5 18.2 20.9 23.8 21.5 20.5 17.9 19.2
 24.7 19.1 25.6 18.  23.9 24.8 19.  21.  20.  18.9 20.2 19.8 19.4 20.6
 22.9 22.2 18.7 22.5 22.1 21.6 19.6 21.3 20.4 19.9 22.4 21.1 20.8 22.
 24.  20.7 23.5]

Number of unique values in Time_Spent_Hours are:227

----------------------------------------------------------------------------------
Days_Since_Last_Login
[ 1  3 19  4  0  2  5 11 18  9  8  7 10 12 13 31  6 20 15 22 17 25 37 16
 36 34 35 43 14 21 49 63 28 42 32 33 27 23 26 39 29 30 24 41 44 61 70 56
 45 46 48 47 38 64 54 60 52 53 40 55 58 50 99 51 97 65 90 66 77 87 57 59
 83 69 62 67]

Number of unique values in Days_Since_Last_Login are:76

----------------------------------------------------------------------------------
Notifications_Checked
[ 6  5  9  7  2  1  3  4  8 10 11  0 12 13 14 16 15 18 17]

Number of unique values in Notifications_Checked are:19

----------------------------------------------------------------------------------
Peer_Interaction_Score
[ 4.3  7.8  6.7  6.4  7.5  6.2  7.2  5.4  6.8  4.9  3.2  7.  10.   4.7
  7.9  2.7  7.7  5.6  3.9  6.1  4.6  8.7  4.8  4.2  5.   8.1  3.8  7.3
  8.3  8.8  9.4  6.9  3.6  2.8  8.   5.1  3.   5.2  5.3  4.1  7.4  3.4
  7.1  9.   6.   9.5  1.7  5.7  4.   8.6  9.2  3.3  8.2  4.4  8.9  7.6
  1.2  2.9  6.5  8.4  6.3  5.9  5.5  5.8  1.   3.7  2.2  2.4  9.8  9.3
  6.6  9.1  2.6  2.5  3.5  3.1  2.1  8.5  9.9  9.6  4.5  1.9  0.7  9.7
  0.9  1.6  2.3  1.3  0.5  2.   0.8  0.   1.5  0.6  1.8  1.1  0.3  1.4
  0.4  0.1  0.2]

Number of unique values in Peer_Interaction_Score are:101

----------------------------------------------------------------------------------
Assignments_Submitted
[ 8.  4.  0.  5.  7.  3. 10.  2. nan  6.  1.]

Number of unique values in Assignments_Submitted are:11

----------------------------------------------------------------------------------
Assignments_Missed
[ 1  6  2 10  5  3  7  0  8  4  9]

Number of unique values in Assignments_Missed are:11

----------------------------------------------------------------------------------
Quiz_Attempts
[ 5  3  4  8  6  2  7  1  9  0 12 10 11 13 15 16 14]

Number of unique values in Quiz_Attempts are:17

----------------------------------------------------------------------------------
Quiz_Score_Avg
[ 80.9  78.4 100.   59.1  84.8  99.6  74.4  76.3  55.8  58.6  82.1  81.5
  73.3  58.3  68.2  74.1  60.7  59.9  71.   63.6  77.5  91.2  73.1  64.3
  67.4  55.1  66.7  86.   80.2  63.4  64.5  87.3  82.9  81.6  77.7  73.7
  76.4  87.6  65.3  51.6  66.5  72.6  83.9  71.7  77.4  75.5  62.6  66.4
  89.7  69.6  55.7  92.9  63.9  67.2  94.4  87.4  67.3  61.6  80.   62.7
  70.2  45.8  73.9  67.8  63.   58.2  70.7  89.3  80.6  74.7  68.5  73.6
  87.8  56.4  73.5  84.7  58.5  78.5  67.6  68.   74.9  77.2  97.6  74.
  86.7  72.5  71.5  88.4  70.6  73.4  62.5  85.6  55.9  82.2  59.7  48.2
  52.5  61.3  79.2  84.6  63.7  57.6  72.3  93.5  80.7  79.9  67.9  71.1
  72.1  55.6  90.1  71.4  69.7  72.8  62.4  53.6  75.9  42.5  56.8  66.
  59.   84.4  91.3  59.8  64.7  73.2  94.7  84.9  85.2  69.   90.9  78.1
  59.4  84.   50.1  99.9  80.1  83.8  67.7  64.9  69.8  74.8  82.7  54.7
  75.   77.1  81.3  83.2  77.3  70.9  89.4  91.4  53.2  61.9  76.   74.3
  87.7  94.2  90.3  87.   85.9  65.8  59.3  76.9  80.4  57.5  51.8  71.9
  64.1  56.1  49.1  56.5  56.9  82.8  90.8  82.6  83.1  79.4  82.4  63.8
  72.9  86.4  69.3  52.9  73.   84.1  64.8  82.5  88.8  65.1  58.4  75.6
  93.6  71.2  60.   63.3  59.5  61.5  94.3  81.8  75.3  52.8  67.1  69.2
  90.5  61.8  57.4  65.4  51.1  60.4  65.6  71.3  49.5  85.8  70.   60.2
  70.4  74.6  69.9  89.6  91.5  63.1  79.6  31.2  90.2  96.2  68.3  50.9
  92.2  65.9  72.7  62.9  86.6  82.   95.2  65.5  81.7  75.2  60.5  81.1
  81.4  72.2  73.8  85.4  78.9  84.5  66.9  90.6  89.9  47.2  79.5  89.
  40.4  68.9  79.1  61.1  88.1  71.8  57.9  77.8  88.6  76.2  94.1  80.8
  68.4  55.4  51.9  81.9  86.8  61.4  79.7  38.7  93.9  78.6  86.5  62.1
  75.8  75.7  74.2  86.1  91.1  49.9  89.8  93.   54.   49.4  68.8  77.6
  62.2  78.8  66.8  64.2  47.   66.6  91.8  79.   93.3  47.1  93.1  93.4
  91.6  53.8  64.4  50.3  76.7  54.8  81.2  78.3  77.   83.5  82.3  57.7
  54.9  69.1  58.7  75.4  65.2  53.5  76.6  48.9  95.7  92.8  83.3  97.7
  58.1  65.7  78.   97.4  80.5  72.   63.2  70.5  49.6  88.3  89.2  87.9
  78.2  79.8  60.8  56.3  83.   81.   70.3  88.7  57.1  94.   84.3  94.9
  59.6  80.3  67.   60.9  45.3  90.   49.   83.4  53.   91.7  66.3  84.2
  54.3  53.4  55.2  66.2  86.9  59.2  58.   92.4  56.6  55.5  56.7  70.8
  69.4  61.7  57.   95.8  53.9  57.8  46.3  68.1  60.6  43.7  60.3  87.1
  51.2  76.8  72.4  61.2  88.2  85.5  62.8  64.   83.6  54.4  77.9  83.7
  64.6  94.6  51.3  97.3  44.9  48.4  54.2  71.6  93.7  31.   86.3  57.2
  53.3  58.9  51.4  99.8  43.3  79.3  76.1  96.3  46.9  98.6  66.1  94.5
  61.   99.   91.9  92.   40.2  48.3  65.   96.   47.5  97.9  44.7  89.5
  49.7  37.7  97.1  67.5  69.5  68.6  53.1  92.6  94.8  86.2  93.8  78.7
  49.8  52.7  55.3  51.5  38.3  95.9  76.5  46.8  87.2  95.4  56.2  52.2
  47.7  46.2  37.8  62.   85.1  90.4  56.   39.5  45.   42.4  37.5  63.5
  85.3  90.7  36.4  40.5  48.1  53.7  50.   96.6  52.   55.   48.8  44.2
  89.1  98.8  88.5  52.1  70.1  92.7  62.3  74.5  96.1  58.8  85.7  48.7
  92.5  75.1  43.8  52.3  40.9  85.   45.4  99.3  42.3  57.3  60.1  46.1
  96.5  50.4  68.7  47.4  51.7  39.   95.   99.2  41.1  34.   45.7  88.
  48.5  44.8  96.4  49.2  50.5  92.1  43.5  99.5  99.1  95.6  96.9  98.4
  98.9  45.5  95.1  36.   54.6  92.3  43.4  51.   97.8  44.3  97.   45.6
  88.9  50.7  52.4  98.3  46.   41.   95.5  99.4  91.   50.2  98.5  98.
  48.6  44.4  45.9  41.9  99.7  39.7  46.6  96.7  87.5  36.7  54.1  39.8
  47.3  32.7  43.1  40.8  46.7  40.6  52.6  43.9  54.5  50.6  93.2  96.8
  50.8  47.9  34.9  45.2  41.7  42.2  43.   31.3  41.2  42.7  44.5  46.5
  48.   33.7  47.8  39.6  32.6  97.2  39.1  95.3  42.8  37.3  42.   98.2
  43.6  49.3  47.6  98.1  37.1  40.7  38.4  38.9  35.6  40.   38.8  98.7
  42.6  44.6  37.2  29.4  39.3  41.3  42.1  36.9  46.4  41.8  44.1  43.2
  38.5  97.5  37.   31.1  40.1  42.9  37.9  34.3  28.9  36.8  41.5  32.8
  38.2  31.5  33.1  34.7  33.4  41.6  44.   25.7  38.6  35.8  40.3  23.5
  36.3  35.4  33.8  35.3  32.2  33.3  29.5  32.4  35.1  30.5  45.1  19.8
  39.9  36.1  38.   30.8  39.4  28.1  24.6  19.6  25.2  29.1  39.2  36.5
  35.2  35.9  41.4  29.6  34.1  32.9  21.2  30.9  35.7  34.8  26.8  34.4
  23.   38.1  30.1  31.6  33.6  33.   25.8  34.5  28.5  32.   32.1  31.4
  27.7  37.6  30.7  30.2  36.2  37.4  25.6  26.3  20.4  26.9  35.5  27.1
  33.5  27.3  24.   34.6  27.6  29.8]

Number of unique values in Quiz_Score_Avg are:714

----------------------------------------------------------------------------------
Project_Grade
[ 71.2  42.5  87.9  51.4  93.   65.6  80.4  72.9  69.4  54.1 100.   54.6
  76.7  66.4  61.4  68.9  69.   53.4  76.4  57.3  51.8  74.2  43.5  66.2
  51.2  62.8  72.5  57.1  67.2  72.4  61.8  48.8  54.4  76.2  94.4  46.7
  67.8  89.2  51.9  65.5  76.6  66.7  57.   75.   73.4  48.6  48.   70.
  83.2  71.   38.3  69.5  72.2  93.6  50.3  81.5  68.4  69.9  54.7  82.9
  50.5  78.5  47.2  84.2  86.   52.9  54.5  72.3  59.6  66.8  70.4  59.
  70.7  76.8  84.5  64.3  50.8  93.5  62.9  69.8  78.3  78.2  74.1  73.3
  59.3  74.9  70.1  46.1  75.4  61.   92.6  42.1  52.8  51.5  79.5  77.1
  99.2  62.3  25.8  44.6  61.2  86.8  65.   65.2  55.5  82.2  39.9  32.7
  85.9  97.7  37.2  47.6  56.   92.7  66.1  77.9  44.1  61.7  74.7  31.8
  16.1  36.   59.8  52.3  53.7  79.2  50.2  47.9  44.3  72.7  76.1  57.4
  64.5  74.3  99.6  73.6  71.6  55.2  63.4  81.4  45.5  64.1  72.1  58.6
  71.1  61.9  58.7  76.5  79.4  47.5  50.1  52.6  73.7  63.2  55.6  64.2
  37.9  71.9  65.9  71.8  87.8  52.1  97.5  49.1  85.6  65.4  41.1  58.9
  52.4  59.5  80.3  36.7  68.3  46.8  78.8  73.2  88.9  72.8  60.3  85.5
  46.5  79.8  97.1  62.5  64.9  58.5  63.5  89.9  65.8  75.5  72.   62.1
  21.3  83.8  53.8  58.2  70.9  60.1  74.   83.3  60.8  66.   87.4  68.5
  63.7  43.2  64.8  83.5  51.   79.   63.6  74.8  40.3  90.8  68.   89.4
  92.2  50.9  86.1  49.9  38.1  73.1  63.8  80.7  82.7  63.1  48.4  51.7
  83.6  81.8  69.1  70.5  54.9  53.5  70.8  78.   56.1  58.   48.2  77.6
  78.4  53.1  70.2  96.5  83.1  49.4  82.   60.6  59.4  80.5  93.9  67.9
  60.2  66.5  80.1  40.9  54.8  85.8  72.6  77.8  55.3  65.1  68.8  73.9
  48.1  33.9  84.6  71.5  52.2  67.1  95.6  83.9  50.7  88.5  63.3  80.8
  59.9  66.9  56.4  61.5  93.2  67.6  44.8  55.4  89.3  59.1  75.8  44.2
  84.7  86.6  35.9  87.3  55.9  77.7  35.1  62.7  75.1  45.9  53.2  92.8
  64.6  57.7  83.7  79.3  67.4  67.5  56.7  84.4  51.3  71.4  59.2  93.4
  77.4  75.2  87.5  56.6  69.2  41.6  93.3  58.8  82.6  98.6  84.   68.6
  49.3  62.   67.7  74.5  65.7  77.2  89.8  47.4  71.7  48.7  76.9  67.3
  96.3  41.   60.7  61.3  58.4  64.7  53.3  35.3  46.3  36.6  53.   85.
  48.3  86.5  50.   63.   84.1  70.3  88.7  92.5  43.7  68.7  47.8  59.7
  90.2  99.   49.6  60.4  49.7  82.4  64.   65.3  44.9  62.4  54.   69.6
  30.7  92.4  96.1  95.7  94.8  40.2  60.5  90.3  53.6  69.3  78.7  67.
  84.3  58.1  47.   51.1  70.6  96.8  78.9  91.4  37.1  56.8  38.9  80.6
  80.2  87.1  57.8  89.5  94.2  93.1  75.3  51.6  83.   38.8  45.   27.
  41.8  68.1  74.4  48.9  79.9  89.7  55.7  61.6  42.3  55.1  57.2  91.3
  43.   53.9  73.   60.   86.2  77.5  52.7  55.8  78.1  57.5  97.   82.5
  89.   87.6  66.3  46.4  50.4  94.5  94.6  91.9  29.4  73.8  56.9  86.3
  35.2  97.8  85.2  87.7  89.1  86.4  61.1  97.3  82.8  99.5  54.2  58.3
  96.9  33.4  66.6  79.1  54.3  34.4  91.   91.7  52.5  95.2  78.6  57.9
  45.8  34.2  32.3  81.9  24.2  35.6  81.3  55.   86.9  26.2  44.4  71.3
  19.8  84.9  49.   60.9  91.5  75.6  33.2  96.   69.7  43.4  41.4  56.5
  49.2  90.   81.7  68.2  86.7  90.5  41.7  74.6  91.6  42.7  81.6  87.2
  63.9  95.3  34.6  40.7  47.3  85.3  29.6  92.1  45.2  76.3  29.7  82.3
  88.1  18.5  39.5  62.2  56.3  75.9  56.2  40.4  64.4  40.5  46.6  80.9
  88.4  44.7  43.8  57.6  37.   46.   85.1  90.9  90.7  77.3  98.8  39.4
  42.   98.1  95.   95.5  76.   99.8  44.5  75.7  81.1  37.8  88.3  92.3
  91.8  85.4  45.7  43.9  36.1  48.5  38.6  30.8  99.4  81.2  37.3  39.3
  92.9  98.5  49.5  39.6  79.6  34.8  36.3  89.6  81.   94.1  45.4  36.2
  90.1  91.2  91.1  50.6  36.8  31.   88.   88.2  82.1  99.7  73.5  98.
  79.7  19.6  88.6  20.3  28.9  26.4  39.2  26.8  83.4  62.6  97.4  37.4
  88.8  40.1  46.9  92.   95.9  14.1  45.3  52.   80.   94.   98.4  38.
  43.6  26.   32.9  38.5  99.3  94.3  98.7  85.7  35.4  43.3  40.   95.1
  22.6  77.   43.1  90.6  33.6  45.1  19.9  32.2  38.4  44.   97.2  96.6
  24.8  49.8  98.9  40.8  42.9  84.8  99.9  29.8  47.7  90.4  39.   47.1
  87.   10.7  40.6  97.9  37.7  96.7  45.6  99.1  36.9  41.5  42.8  29.
  36.4  30.6  41.3  93.7  96.2  18.2  42.2  42.4  35.   46.2  30.2  41.2
  25.2  25.   94.7  95.8  30.   34.   32.6  93.8  30.4  34.5  98.3  20.6
  37.6  39.7  22.9  37.5  29.3  24.   34.3  23.4  28.2  19.2  31.7  41.9
  26.6  22.   35.5  25.9  94.9  38.2  31.6  21.4  27.7  16.7  98.2  31.3
  26.3  28.1  23.3  31.9  25.7  42.6  31.1  28.6  32.   97.6  28.   28.8
  31.4  33.8  96.4  95.4  33.   33.3  31.2  22.3  27.9  29.9  15.5  35.8
  25.4  32.4  34.1  38.7  35.7  19.5  39.8  30.1  39.1  32.8  36.5  21.8
  28.7  20.1  29.1  29.5  23.2  24.6  30.5  28.3  29.2  30.9  34.7  21.9
  33.1  21.7  25.6  30.3  15.6  23.6  20.8  34.9  27.5  23.9  33.7  27.6
  25.3  20.4  24.4  33.5  23.7  24.7  24.3  31.5  18.7  15.7  18.4  26.5
  11.5  22.8  19.7  23.   22.5  18.   24.5   6.8  26.7  27.8  26.1   8.7
  13.4  32.5  23.1  16.6  26.9  27.4  22.4  28.4  22.7  28.5  18.9  19.4
  23.8  32.1  10.1  27.2  11.3  20.5  13.9  16.9  17.3  20.7  14.4  20.9
  15.9  12.9  21.1  20.2  18.8   5.3  16.   22.2  18.3  23.5  27.1  27.3
  25.1  20.   19.   22.1  13.7  16.4  21.    9.5  21.5   5.5  17.4  21.2
  14.7  24.9  17.1  11.9  14.9   8.5  14.8  25.5  14.2  19.3  10.5  11.6
   7.4  17.6  11.4  15.4  13.6  16.8  14.5  18.6  17.8  17.9   0.   15.2
  11.2]

Number of unique values in Project_Grade are:865

----------------------------------------------------------------------------------
Progress_Percentage
[70.8 55.6 78.8 24.7 64.9 75.3 55.1 73.4 92.3 42.9 52.  55.2 40.6 64.2
 40.9 65.8 63.3 34.9 58.  32.8 80.  44.8 56.2 37.4 56.7 63.1 53.7 45.5
 29.7 72.7 66.7 76.4 33.1 62.9 44.4 46.7 59.2 66.4 61.8 30.9 67.6 64.6
 52.8 36.9 49.7 50.  64.8 40.7 60.5 69.4 44.9 56.3 63.5 45.9 58.6 70.4
 72.8 50.4 53.1 59.7 40.8 53.5 51.6 54.  65.5 75.6 45.6 60.6 64.  53.
 22.6 50.6 45.  39.2 50.1 67.8 81.2 69.  66.5 67.3 48.4 52.2 45.8 85.4
 45.7 62.4 68.4 61.2 39.1 28.5 39.3 37.9 44.1 38.4 79.6 68.2 43.4 70.6
 49.1 60.1 38.3 43.7 25.5 50.3 80.8 34.5 52.6 73.9 51.3 42.5 68.8 55.4
 47.3 31.3 66.8 58.9 51.2 66.1 40.4 34.1 59.5 16.4 46.9 41.3 54.8 37.3
 53.8 51.7 78.7 67.4 70.5 49.9 24.4 61.9 61.6 63.9 44.  48.9 26.5 56.8
 47.5 36.4 37.2 46.4 45.2 58.1 57.1 34.2 64.3 57.2 63.2 72.2 39.  30.8
 41.  31.7 70.1 32.  49.2 53.3 36.5 46.3 58.7 51.4 68.6 49.4 47.1 59.3
 69.3 56.1 46.1 69.7 48.5 76.6 43.2 61.  51.8 69.2 52.4 43.6 39.9 62.3
 75.4 49.6 52.7 71.8 46.2 51.5 54.9 63.  54.5 62.1 62.  57.3 41.4 43.9
 43.3 35.2 49.  47.7 28.  48.3 82.5 56.5 70.3 58.5 66.9 38.9 35.6 40.2
 77.6 61.7 41.2 38.  50.2 42.4 71.1 71.4 32.2 38.7 37.7 68.1 69.1 43.5
 73.6 56.9 59.1 57.6 59.9 42.7 36.1 53.4 36.8 60.2 58.3 36.6 57.4 63.6
 72.3 62.5 44.3 33.7 61.1 63.8 58.4 53.2 68.  57.5 76.8 47.  54.7 76.2
 56.6 54.3 62.7 75.2 62.8 44.6 61.3 82.9 44.7 64.4 45.4 47.6 46.8 57.
 69.9 31.  65.9 47.2 42.  52.1 48.2 33.  59.8 93.  59.  48.7 59.6 55.5
 78.9 73.  60.8 73.5 53.6 65.7 68.7 48.  51.1 76.3 25.1 63.7 54.2 76.7
 74.3 55.3 65.1 66.2 72.9 27.1 60.9 58.2 66.6 55.9 68.9 60.  48.1 70.7
 40.5 77.9 54.4 39.7 28.4 62.2 62.6 30.1 50.8 50.9 30.7 66.3 46.5 45.1
 49.5 67.7 79.9 73.1 46.6 47.9 74.4 65.3 35.4 50.7 51.  70.  56.4 51.9
 65.6 32.1 55.8 67.2 55.  89.  41.8 68.5 39.5 53.9 31.1 49.3 72.1 65.2
 69.8 38.1 75.7 60.3 70.9 41.1 66.  75.  54.1 77.  83.6 65.4 40.3 33.4
 52.3 39.8 44.2 81.1 24.5 44.5 72.4 73.8 42.3 35.1 50.5 37.8 34.  55.7
 20.2 64.5 27.3 39.6 26.7 31.8 47.4 41.7 60.4 64.1 26.6 78.4 67.  79.4
 58.8 71.3 35.  47.8 75.5 77.8 78.3 64.7 78.1 49.8 71.  38.5 43.8 81.6
 29.2 32.3 32.4 74.1 60.7 71.2 30.5 43.1 46.  34.6 48.8 33.9 36.2 71.9
 69.6 73.3 57.7 45.3 63.4 70.2 30.2 57.9 31.6 34.8 42.1 93.4 36.7 38.2
 83.5 42.2 41.6 67.9 77.2  nan 52.9 33.6 48.6 33.5 30.  57.8 10.2 40.
 39.4 33.3 74.6 34.4 72.6 27.9 24.9 65.  81.5 41.5 41.9 29.9 43.  54.6
 67.1 77.1 25.8 78.2 71.5 74.5 36.  74.9 79.  27.2 16.  73.7 29.8 80.9
 29.  22.7 84.9 61.4 80.4 88.4 85.5 28.8 92.9 77.7 35.3 80.5 42.6 69.5
 35.8 82.3 32.9 40.1 21.4 85.2 28.2 71.6 37.1 74.7 72.5 68.3 37.5 34.3
 37.  61.5 38.6 26.9 17.5 38.8 74.2 32.7 30.6 67.5 80.1 26.4 76.  17.9
 56.  17.1 35.7 86.6 79.3 59.4 72.  21.5 73.2 78.5 42.8 37.6 88.5 16.6
 32.6 20.8 77.4 79.2 32.5 25.2 36.3 76.1 78.6 78.  31.5 75.9 23.2 90.8
 27.6 22.5 29.5 21.  14.4 82.2 34.7 75.8 25.  31.4 33.2 29.1 35.5 31.9
 74.8 24.2 87.1 30.4 19.9 74.  23.7 76.9 81.3 82.6 26.  29.4 84.4 27.5
 22.4 81.9 23.1 79.8 71.7 28.3 88.8 84.8 25.7 89.1 77.3 21.2 27.8 87.
 86.8 26.8 25.6 27.7 29.3 26.1 20.3 79.1 33.8 24.8 81.8 20.4 21.9 83.7
 83.2 80.3 82.7 79.5 19.3 25.4 19.5 82.1 89.7 81.4 24.6 23.8 12.  35.9
 89.5 77.5  9.7 11.2 85.3 27.  76.5 86.7 22.  28.1 82.  87.8 18.6 90.
 90.2 19.7 25.9 21.1 92.8 86.4 75.1 23.4 19.4 24.1 28.7 84.1 83.  20.9
 84.2 84.  88.6 21.8 83.3 31.2 22.9 85.  81.  24.  25.3 18.2 80.6 89.4
 83.1 16.1 30.3 80.7 83.4 17.3 90.1 16.8 17.7 18.5 94.4 28.6 21.7 87.9
 19.6 82.4 15.8 26.2 82.8 29.6 81.7 14.7 80.2 84.5 18.3 84.3 23.3 22.3
 10.8 24.3 20.5 79.7 21.6 20.7 91.3 23.5 83.8 23.9 19.  13.9 23.6 88.3
 28.9 87.3 18.  95.4 14.3 83.9 88.  86.9 19.1 15.4 88.7 85.1 92.2 85.7
 86.1 20.1 16.7 22.1 91.5 84.6 86.  84.7 86.5 87.6 10.4 26.3 87.2 91.4
 97.4 86.2 20.6 23.  18.7 16.9 19.2 15.9 85.8 90.9 86.3 13.7 18.8 13.6
 27.4 18.9 89.6 21.3 85.9 87.4 17.6 93.3 17.4 88.9 19.8 11.  20.  90.5
 12.9 91.7 89.8 18.4 94.6 22.8 22.2 17.2 88.1 11.3 91.1 14.2 15.2 16.3
 96.6 15.3 10.   8.4 87.5 14.5 18.1 91.2 89.9 90.4 14.6  9.4 90.3 12.3
 85.6  8.1 96.1  7.6 90.6 14.8 87.7 94.9 15.  13.1 88.2 13.4 15.5 12.8
 16.5 15.7 98.6 89.3 94.  94.8 15.1 11.7 96.5 17.  11.8 89.2 11.1 91.8
 92.  91.  12.6 92.4 12.7 13.5 90.7 91.6 17.8 10.6 93.2]

Number of unique values in Progress_Percentage are:823

----------------------------------------------------------------------------------
Rewatch_Count
[ 0.  2.  4.  5.  1.  3.  8.  7. nan  9. 12. 11. 13. 15.]

Number of unique values in Rewatch_Count are:14

----------------------------------------------------------------------------------
Enrollment_Date
['01-06-2024' '27-04-2025' '20-01-2024' '13-05-2025' '19-12-2024'
 '23-10-2023' '24-03-2024' '09-11-2024' '13-07-2024' '07-11-2024'
 '11-07-2025' '02-12-2023' '11-06-2024' '24-10-2024' '16-07-2024'
 '23-06-2024' '02-08-2025' '13-12-2023' '30-11-2024' '19-05-2025'
 '05-08-2025' '05-06-2025' '06-03-2024' '06-02-2024' '22-12-2024'
 '07-02-2025' '28-10-2023' '23-01-2025' '12-03-2025' '18-07-2025'
 '07-05-2024' '12-04-2025' '08-02-2024' '18-06-2024' '05-01-2024'
 '14-04-2025' '01-05-2025' '23-12-2024' '22-09-2025' '03-09-2025'
 '17-02-2024' '10-06-2025' '28-12-2023' '19-09-2024' '25-02-2025'
 '23-05-2025' '06-12-2024' '13-10-2024' '11-12-2023' '05-02-2024'
 '08-03-2024' '02-01-2024' '25-01-2024' '22-05-2024' '19-10-2023'
 '24-01-2025' '08-05-2025' '15-12-2023' '02-09-2024' '24-04-2024'
 '13-06-2024' '31-05-2025' '20-08-2025' '10-07-2024' '14-07-2024'
 '27-01-2025' '26-10-2023' '31-12-2023' '08-10-2024' '26-03-2024'
 '04-07-2025' '18-08-2024' '03-02-2024' '19-05-2024' '08-12-2023'
 '01-12-2023' '11-07-2024' '27-12-2024' '23-09-2025' '04-06-2025'
 '27-08-2024' '26-11-2023' '30-01-2024' '15-06-2024' '12-12-2024'
 '11-12-2024' '04-02-2024' '31-05-2024' '24-08-2024' '28-11-2024'
 '28-07-2024' '18-04-2025' '18-06-2025' '05-01-2025' '29-05-2025'
 '18-10-2023' '02-09-2025' '27-08-2025' '21-02-2024' '15-09-2024'
 '30-04-2024' '12-04-2024' '17-10-2023' '01-06-2025' '11-01-2025'
 '07-04-2024' '12-07-2025' '11-11-2023' '30-12-2023' '03-05-2024'
 '18-08-2025' '28-09-2025' '18-02-2024' '12-01-2025' '26-12-2024'
 '14-02-2025' '30-07-2025' '19-07-2025' '04-08-2024' '22-04-2025'
 '26-09-2025' '07-12-2024' '15-05-2024' '19-11-2024' '25-06-2025'
 '04-12-2024' '15-07-2025' '16-05-2025' '31-01-2025' '20-05-2024'
 '08-11-2024' '08-01-2024' '09-10-2024' '14-08-2025' '08-05-2024'
 '12-02-2025' '05-07-2024' '11-02-2024' '31-08-2024' '26-11-2024'
 '20-07-2025' '16-09-2025' '18-05-2025' '30-10-2024' '20-12-2024'
 '28-07-2025' '03-04-2025' '23-02-2025' '17-07-2024' '13-01-2024'
 '03-02-2025' '15-11-2023' '21-05-2024' '29-04-2024' '07-05-2025'
 '02-03-2025' '30-05-2024' '03-06-2025' '25-12-2024' '20-07-2024'
 '01-05-2024' '26-07-2024' '27-01-2024' '21-08-2024' '23-02-2024'
 '03-08-2024' '22-12-2023' '18-09-2025' '23-08-2024' '16-12-2024'
 '04-04-2025' '02-07-2025' '01-01-2024' '16-06-2025' '01-08-2024'
 '15-07-2024' '26-08-2024' '07-08-2024' '02-10-2025' '07-01-2025'
 '11-09-2024' '06-09-2025' '04-09-2025' '13-04-2024' '27-07-2024'
 '03-03-2025' '09-09-2024' '02-05-2025' '12-09-2024' '30-03-2025'
 '06-08-2024' '30-08-2024' '03-06-2024' '22-03-2024' '22-07-2024'
 '22-06-2024' '21-06-2025' '27-12-2023' '29-09-2024' '17-08-2024'
 '17-03-2024' '04-05-2025' '27-04-2024' '10-12-2024' '10-11-2024'
 '20-03-2024' '12-06-2025' '09-01-2025' '12-03-2024' '25-09-2025'
 '27-02-2025' '24-07-2025' '18-10-2024' '15-09-2025' '10-06-2024'
 '29-08-2025' '04-10-2024' '13-11-2024' '21-08-2025' '05-08-2024'
 '29-07-2025' '22-08-2025' '08-08-2025' '14-08-2024' '29-02-2024'
 '21-02-2025' '04-06-2024' '17-01-2024' '06-06-2025' '20-12-2023'
 '08-12-2024' '19-04-2025' '29-09-2025' '23-03-2024' '08-07-2025'
 '26-05-2024' '26-01-2024' '19-02-2024' '01-08-2025' '06-05-2025'
 '27-02-2024' '23-08-2025' '16-06-2024' '09-12-2023' '21-12-2023'
 '15-06-2025' '07-07-2025' '17-11-2023' '13-09-2024' '17-03-2025'
 '21-01-2025' '01-09-2024' '13-02-2025' '01-01-2025' '14-05-2025'
 '28-08-2025' '19-03-2025' '12-11-2023' '28-08-2024' '11-03-2024'
 '26-07-2025' '05-11-2024' '24-03-2025' '10-09-2025' '13-05-2024'
 '04-07-2024' '30-12-2024' '08-06-2024' '25-11-2024' '06-04-2024'
 '22-03-2025' '07-08-2025' '06-05-2024' '17-05-2025' '04-09-2024'
 '11-10-2024' '24-11-2023' '01-02-2025' '28-11-2023' '26-04-2024'
 '19-12-2023' '02-10-2024' '26-06-2024' '27-03-2024' '19-08-2025'
 '11-01-2024' '17-09-2024' '17-09-2025' '20-03-2025' '23-05-2024'
 '05-03-2024' '28-01-2024' '08-04-2025' '10-07-2025' '19-04-2024'
 '18-01-2025' '08-02-2025' '10-01-2025' '14-03-2025' '27-06-2025'
 '20-02-2025' '28-03-2025' '06-01-2025' '02-11-2024' '14-11-2023'
 '12-08-2025' '05-11-2023' '01-07-2025' '11-03-2025' '27-05-2025'
 '25-04-2024' '07-04-2025' '16-10-2024' '21-07-2025' '21-09-2024'
 '27-11-2024' '09-08-2025' '04-12-2023' '15-11-2024' '27-10-2024'
 '24-12-2024' '18-04-2024' '16-09-2024' '29-11-2023' '14-12-2023'
 '25-03-2025' '19-11-2023' '10-04-2024' '08-09-2024' '09-08-2024'
 '03-10-2025' '05-03-2025' '06-02-2025' '23-12-2023' '05-02-2025'
 '03-05-2025' '02-01-2025' '24-07-2024' '02-06-2025' '08-08-2024'
 '17-12-2023' '12-05-2024' '07-09-2024' '27-03-2025' '07-12-2023'
 '27-06-2024' '09-11-2023' '19-06-2024' '31-07-2024' '25-10-2024'
 '22-05-2025' '29-07-2024' '24-01-2024' '10-05-2025' '21-03-2025'
 '05-07-2025' '03-11-2023' '20-09-2025' '18-11-2023' '10-02-2025'
 '28-04-2025' '13-09-2025' '25-06-2024' '16-08-2024' '28-02-2025'
 '23-07-2024' '14-03-2024' '18-02-2025' '08-06-2025' '13-12-2024'
 '15-08-2025' '16-01-2024' '23-11-2023' '25-04-2025' '16-03-2025'
 '20-05-2025' '05-12-2024' '01-09-2025' '17-02-2025' '21-10-2024'
 '29-04-2025' '02-04-2024' '12-09-2025' '20-10-2023' '03-10-2024'
 '26-12-2023' '03-03-2024' '06-11-2024' '21-04-2025' '16-04-2024'
 '02-02-2025' '05-12-2023' '12-06-2024' '28-05-2025' '18-12-2023'
 '19-01-2025' '09-06-2025' '28-04-2024' '21-06-2024' '19-10-2024'
 '04-08-2025' '22-04-2024' '23-07-2025' '27-05-2024' '31-07-2025'
 '02-11-2023' '16-08-2025' '09-04-2024' '20-09-2024' '16-05-2024'
 '17-06-2025' '04-11-2024' '27-07-2025' '19-07-2024' '20-06-2025'
 '11-05-2025' '04-03-2025' '21-12-2024' '30-06-2024' '09-07-2025'
 '18-09-2024' '13-08-2025' '27-09-2025' '30-07-2024' '18-05-2024'
 '22-08-2024' '24-09-2025' '09-09-2025' '11-09-2025' '12-01-2024'
 '22-02-2025' '17-11-2024' '11-04-2024' '29-05-2024' '07-03-2025'
 '29-01-2024' '08-01-2025' '28-12-2024' '19-06-2025' '11-08-2024'
 '26-03-2025' '16-12-2023' '18-07-2024' '28-10-2024' '26-09-2024'
 '24-02-2025' '03-12-2023' '26-01-2025' '31-10-2024' '22-01-2024'
 '25-10-2023' '01-03-2024' '20-02-2024' '29-12-2024' '30-06-2025'
 '29-10-2023' '21-05-2025' '15-12-2024' '29-08-2024' '17-06-2024'
 '12-02-2024' '04-10-2025' '04-04-2024' '12-12-2023' '12-11-2024'
 '22-11-2023' '16-02-2024' '04-02-2025' '10-08-2024' '04-05-2024'
 '10-03-2024' '01-04-2025' '23-11-2024' '25-08-2025' '03-09-2024'
 '06-09-2024' '11-05-2024' '15-10-2024' '09-07-2024' '13-01-2025'
 '26-05-2025' '16-11-2023' '28-06-2024' '30-09-2025' '06-11-2023'
 '06-07-2025' '15-04-2024' '11-08-2025' '11-06-2025' '13-08-2024'
 '08-03-2025' '06-10-2024' '15-01-2024' '30-05-2025' '20-11-2023'
 '01-02-2024' '29-03-2024' '27-09-2024' '03-01-2025' '23-01-2024'
 '14-01-2024' '19-02-2025' '12-07-2024' '30-08-2025' '23-04-2025'
 '17-04-2025' '21-11-2023' '24-05-2025' '04-01-2025' '24-12-2023'
 '14-09-2024' '09-02-2024' '25-11-2023' '01-11-2024' '18-03-2025'
 '09-05-2024' '20-10-2024' '01-04-2024' '16-07-2025' '25-09-2024'
 '22-06-2025' '25-02-2024' '21-04-2024' '14-02-2024' '07-01-2024'
 '03-12-2024' '15-04-2025' '10-09-2024' '25-08-2024' '01-10-2024'
 '28-02-2024' '16-04-2025' '24-08-2025' '09-03-2025' '08-04-2024'
 '14-07-2025' '28-03-2024' '31-03-2025' '03-08-2025' '16-01-2025'
 '04-11-2023' '17-08-2025' '22-01-2025' '09-12-2024' '21-10-2023'
 '31-01-2024' '24-06-2025' '17-07-2025' '30-10-2023' '10-12-2023'
 '10-08-2025' '25-05-2025' '19-01-2024' '10-02-2024' '05-10-2025'
 '07-07-2024' '07-06-2025' '29-06-2025' '25-03-2024' '09-03-2024'
 '01-07-2024' '02-05-2024' '26-04-2025' '29-03-2025' '08-11-2023'
 '02-08-2024' '23-06-2025' '20-04-2024' '13-04-2025' '30-11-2023'
 '16-02-2025' '24-10-2023' '02-07-2024' '17-04-2024' '18-01-2024'
 '06-03-2025' '01-11-2023' '28-09-2024' '05-09-2025' '15-02-2025'
 '20-06-2024' '13-07-2025' '23-03-2025' '30-01-2025' '10-03-2025'
 '18-03-2024' '22-07-2025' '06-07-2024' '13-03-2024' '29-12-2023'
 '05-09-2024' '24-11-2024' '22-10-2024' '10-01-2024' '21-07-2024'
 '05-06-2024' '04-03-2024' '30-04-2025' '06-01-2024' '09-02-2025'
 '14-11-2024' '24-04-2025' '07-10-2024' '23-04-2024' '20-04-2025'
 '01-03-2025' '06-12-2023' '02-02-2024' '25-07-2024' '04-01-2024'
 '23-09-2024' '22-11-2024' '16-03-2024' '13-03-2025' '05-04-2025'
 '31-10-2023' '17-12-2024' '09-05-2025' '15-01-2025' '22-10-2023'
 '08-07-2024' '26-02-2024' '25-07-2025' '03-11-2024' '01-10-2025'
 '06-08-2025' '10-10-2024' '26-02-2025' '07-03-2024' '26-08-2025'
 '13-06-2025' '20-08-2024' '11-04-2025' '31-12-2024' '20-11-2024'
 '21-09-2025' '03-07-2025' '14-01-2025' '06-06-2024' '25-05-2024'
 '05-05-2025' '06-10-2025' '10-11-2023' '27-11-2023' '03-01-2024'
 '31-03-2024' '06-04-2025' '21-01-2024' '19-09-2025' '08-09-2025'
 '29-11-2024' '09-06-2024' '25-12-2023' '11-11-2024' '18-11-2024'
 '30-09-2024' '24-06-2024' '07-11-2023' '14-06-2024' '20-01-2025'
 '28-01-2025' '15-03-2025' '24-02-2024' '19-03-2024' '02-12-2024'
 '19-08-2024' '26-06-2025' '07-09-2025' '21-11-2024' '30-03-2024'
 '27-10-2023' '01-12-2024' '09-04-2025' '28-06-2025' '22-02-2024'
 '02-06-2024' '14-06-2025' '22-09-2024' '17-10-2024' '24-09-2024'
 '14-04-2024' '07-02-2024' '09-01-2024' '10-05-2024' '03-04-2024'
 '03-07-2024' '07-06-2024' '12-05-2025' '28-05-2024' '12-10-2024'
 '23-10-2024' '14-09-2025' '14-12-2024' '05-10-2024' '17-05-2024'
 '15-03-2024' '14-10-2024' '29-06-2024' '31-08-2025' '26-10-2024'
 '24-05-2024' '29-10-2024' '15-02-2024' '18-12-2024' '11-02-2025'
 '13-11-2023' '16-11-2024' '15-08-2024' '13-02-2024' '02-03-2024'
 '12-08-2024' '10-04-2025' '14-05-2024' '05-04-2024' '05-05-2024'
 '15-05-2025' '25-01-2025' '21-03-2024' '17-01-2025' '29-01-2025'
 '02-04-2025']

Number of unique values in Enrollment_Date are:721

----------------------------------------------------------------------------------
Payment_Mode
['Scholarship' 'Credit Card' 'NetBanking' 'UPI' 'Debit Card' 'Free']

Number of unique values in Payment_Mode are:6

----------------------------------------------------------------------------------
Fee_Paid
['No' 'Yes']

Number of unique values in Fee_Paid are:2

----------------------------------------------------------------------------------
Discount_Used
['No' 'Yes']

Number of unique values in Discount_Used are:2

----------------------------------------------------------------------------------
Payment_Amount
[1740 6147 4280 ... 1786 2336 2424]

Number of unique values in Payment_Amount are:6884

----------------------------------------------------------------------------------
App_Usage_Percentage
[ 49.  86.  85.  42.  91.  74.  83.  68.  78.  48.  88.  60. 100.  32.
  40.  59.  77.  61.  70.  66.  46.  38.  93.  89.  67.  87.  47.  25.
  51.  29.  57.  62.  79.  76.  90.  65.  82.  54.  73.  50.  98.  80.
  69.  35.  56.  63.  81.  75.  28.  64.  45.  39.  31.  44.  71.  97.
  34.  41.  96.  12.  94.  24.  58.  92.  99.  84.  72.  30.  95.  55.
  53.  43.  52.  36.  37.  15.  33.  nan   7.  21.  22.  13.  26.  27.
  14.  23.  16.  19.  20.  17.  11.  18.   5.  10.   2.   8.   4.   3.
   0.   9.]

Number of unique values in App_Usage_Percentage are:100

----------------------------------------------------------------------------------
Reminder_Emails_Clicked
[ 3  0  1  2  5  4  7  6  9  8 11 13 10 12]

Number of unique values in Reminder_Emails_Clicked are:14

----------------------------------------------------------------------------------
Support_Tickets_Raised
[4 0 3 2 1 5 6 7 8]

Number of unique values in Support_Tickets_Raised are:9

----------------------------------------------------------------------------------
Satisfaction_Rating
[3.5 4.5 5.  3.8 4.  4.8 4.7 4.4 4.6 3.6 4.2 3.4 nan 3.9 4.3 4.9 3.7 4.1
 2.9 3.1 3.3 3.  2.4 2.1 3.2 1.4 2.  1.9 2.7 2.3 2.2 2.6 2.5 1.8 1.6 1.5
 1.3 1.  1.1 1.2]

Number of unique values in Satisfaction_Rating are:40

----------------------------------------------------------------------------------
Completed
['Completed' 'Not Completed']

Number of unique values in Completed are:2

----------------------------------------------------------------------------------

Discount_Used
No     80058
Yes    19942
Name: count, dtype: int64

23

17

np.float64(10.712000000000002)

Student_ID                      0.000
Name                            0.000
Gender                          0.000
Age                             4.774
Education_Level                 0.000
Employment_Status               0.000
City                            0.000
Device_Type                     0.000
Internet_Connection_Quality     0.000
Course_ID                       0.000
Course_Name                     0.000
Category                        0.000
Course_Level                    0.000
Course_Duration_Days            0.000
Instructor_Rating               0.000
Login_Frequency                 0.112
Average_Session_Duration_Min    0.000
Video_Completion_Rate           0.000
Discussion_Participation        0.955
Time_Spent_Hours                0.000
Days_Since_Last_Login           0.000
Notifications_Checked           0.000
Peer_Interaction_Score          0.000
Assignments_Submitted           0.952
Assignments_Missed              0.000
Quiz_Attempts                   0.000
Quiz_Score_Avg                  0.000
Project_Grade                   0.000
Progress_Percentage             0.335
Rewatch_Count                   2.476
Enrollment_Date                 0.000
Payment_Mode                    0.000
Fee_Paid                        0.000
Discount_Used                   0.000
Payment_Amount                  0.000
App_Usage_Percentage            0.026
Reminder_Emails_Clicked         0.000
Support_Tickets_Raised          0.000
Satisfaction_Rating             1.082
Completed                       0.000
dtype: float64

np.float64(0.417360783162141)

mean      25.335948
median    25.000000
Name: Age, dtype: float64

Age
17.0    9145
25.0    6649
26.0    6616
24.0    6460
27.0    6325
28.0    6168
23.0    6043
29.0    5650
22.0    5507
30.0    4973
21.0    4919
20.0    4427
31.0    4384
19.0    3731
33.0    3123
18.0    3122
34.0    2410
35.0    1869
36.0    1393
38.0     769
39.0     564
40.0     373
41.0     237
42.0     147
43.0      85
44.0      68
45.0      35
46.0      24
48.0       5
49.0       4
52.0       1
Name: count, dtype: int64

0

np.float64(0.42700657919129203)

mean      4.77703
median    5.00000
Name: Login_Frequency, dtype: float64

Login_Frequency
4.0     21566
5.0     20514
3.0     16089
6.0     15306
7.0      9209
2.0      7677
8.0      4630
9.0      2110
1.0      1706
10.0      776
11.0      251
0.0        43
14.0        8
15.0        3
Name: count, dtype: int64

0

np.float64(0.625828033057328)

mean      2.283619
median    2.000000
Name: Discussion_Participation, dtype: float64

Discussion_Participation
2.0     25525
1.0     22936
3.0     19547
4.0     11710
0.0     10745
5.0      5718
6.0      2453
8.0       307
9.0        98
11.0        4
12.0        2
Name: count, dtype: int64

0

np.float64(-0.04468387727492165)

mean      4.734826
median    5.000000
Name: Assignments_Submitted, dtype: float64

np.float64(5.0)

Assignments_Submitted
5.0     23100
4.0     21248
6.0     17972
3.0     14098
7.0     10064
2.0      6457
8.0      4000
1.0      1755
0.0       230
10.0      124
Name: count, dtype: int64

Assignments_Submitted
5.0     23335
4.0     21463
6.0     18130
3.0     14243
7.0     10151
2.0      6518
8.0      4036
1.0      1769
0.0       231
10.0      124
Name: count, dtype: int64

0

np.float64(-0.04771359142045723)

mean      53.827156
median    54.000000
Name: Progress_Percentage, dtype: float64

Progress_Percentage
56.7    361
53.8    349
57.7    346
53.6    336
54.9    333
       ... 
90.7      1
91.6      1
17.8      1
10.6      1
93.2      1
Name: count, Length: 822, dtype: int64

0

np.float64(0.6820974783722953)

mean      2.229492
median    2.000000
Name: Rewatch_Count, dtype: float64

Rewatch_Count
2.0     25704
1.0     22993
3.0     19694
4.0     11588
0.0     10619
5.0      5694
7.0       866
8.0       260
9.0        96
11.0        6
12.0        2
13.0        1
15.0        1
Name: count, dtype: int64

0

np.float64(-0.2466866425852851)

mean      67.876098
median    68.000000
Name: App_Usage_Percentage, dtype: float64

App_Usage_Percentage
100.0    6127
72.0     2086
68.0     2044
65.0     1961
64.0     1949
         ... 
5.0        21
7.0        18
4.0        15
3.0         9
2.0         8
Name: count, Length: 99, dtype: int64

0

np.float64(-0.6078686559974532)

mean      4.147066
median    4.200000
Name: Satisfaction_Rating, dtype: float64

Satisfaction_Rating
5.0    17616
4.2     4914
4.3     4862
4.4     4858
4.1     4835
4.0     4796
4.5     4637
3.9     4563
4.6     4489
3.8     4233
4.7     4069
3.7     4042
3.6     3778
4.8     3772
3.5     3450
4.9     3443
3.4     3085
3.3     2581
3.2     2279
3.1     1964
3.0     1566
2.9     1343
2.7      880
2.6      746
2.5      530
2.4      430
2.3      329
2.2      279
2.1      192
2.0      140
1.9       92
1.8       59
1.6       24
1.5       18
1.3       10
1.4        6
1.0        6
1.1        1
1.2        1
Name: count, dtype: int64

0

np.int64(0)

Skewness of the column Age is : 0.4365762039036042
Skewness of the column Course_Duration_Days is : 0.47089430960814793
Skewness of the column Instructor_Rating is : -0.40421274031438154
Skewness of the column Login_Frequency is : 0.426828197112078
Skewness of the column Average_Session_Duration_Min is : 0.019499390538360858
Skewness of the column Video_Completion_Rate is : -0.3644220917501344
Skewness of the column Discussion_Participation is : 0.6338066082585112
Skewness of the column Time_Spent_Hours is : 1.1146121667660545
Skewness of the column Days_Since_Last_Login is : 2.295673564912052
Skewness of the column Notifications_Checked is : 0.5083082227197634
Skewness of the column Peer_Interaction_Score is : -0.17442136310527653
Skewness of the column Assignments_Submitted is : -0.04404763707114829
Skewness of the column Assignments_Missed is : -0.059119619329094875
Skewness of the column Quiz_Attempts is : 0.5715377344295525
Skewness of the column Quiz_Score_Avg is : -0.09184007660386516
Skewness of the column Project_Grade is : -0.11886932931229066
Skewness of the column Progress_Percentage is : -0.04773031995606081
Skewness of the column Rewatch_Count is : 0.6821754556498142
Skewness of the column Payment_Amount is : -0.3692041551572178
Skewness of the column App_Usage_Percentage is : -0.24672377018863956
Skewness of the column Reminder_Emails_Clicked is : 0.7273282007663202
Skewness of the column Support_Tickets_Raised is : 1.1340954795148297
Skewness of the column Satisfaction_Rating is : -0.6070948605295641

18226

81774

Skewness of the column Age is : 0.4365762039036042
Skewness of the column Course_Duration_Days is : 0.47089430960814793
Skewness of the column Instructor_Rating is : -0.40421274031438154
Skewness of the column Login_Frequency is : 0.426828197112078
Skewness of the column Average_Session_Duration_Min is : 0.019499390538360858
Skewness of the column Video_Completion_Rate is : -0.3644220917501344
Skewness of the column Discussion_Participation is : 0.6338066082585112
Skewness of the column Time_Spent_Hours is : 1.1146121667660545
Skewness of the column Days_Since_Last_Login is : 2.295673564912052
Skewness of the column Notifications_Checked is : 0.5083082227197634
Skewness of the column Peer_Interaction_Score is : -0.17442136310527653
Skewness of the column Assignments_Submitted is : -0.04404763707114829
Skewness of the column Assignments_Missed is : -0.059119619329094875
Skewness of the column Quiz_Attempts is : 0.5715377344295525
Skewness of the column Quiz_Score_Avg is : -0.09184007660386516
Skewness of the column Project_Grade is : -0.11886932931229066
Skewness of the column Progress_Percentage is : -0.04773031995606081
Skewness of the column Rewatch_Count is : 0.6821754556498142
Skewness of the column Payment_Amount is : -0.3692041551572178
Skewness of the column App_Usage_Percentage is : -0.24672377018863956
Skewness of the column Reminder_Emails_Clicked is : 0.7273282007663202
Skewness of the column Support_Tickets_Raised is : 1.1340954795148297
Skewness of the column Satisfaction_Rating is : -0.6070948605295641

Kurtosis of the column Age is : 0.002370893226747217
Kurtosis of the column Course_Duration_Days is : -0.8230337824788738
Kurtosis of the column Instructor_Rating is : -1.2105761221919056
Kurtosis of the column Login_Frequency is : 0.08374155311262621
Kurtosis of the column Average_Session_Duration_Min is : -0.07360773413163768
Kurtosis of the column Video_Completion_Rate is : -0.5538516410283587
Kurtosis of the column Discussion_Participation is : 0.37972877691821516
Kurtosis of the column Time_Spent_Hours is : 0.7348942042977593
Kurtosis of the column Days_Since_Last_Login is : 8.583771205154857
Kurtosis of the column Notifications_Checked is : 0.3155318308388493
Kurtosis of the column Peer_Interaction_Score is : -0.33895835091637583
Kurtosis of the column Assignments_Submitted is : -0.34247000403151207
Kurtosis of the column Assignments_Missed is : -0.2223412316831186
Kurtosis of the column Quiz_Attempts is : 0.3395520800140801
Kurtosis of the column Quiz_Score_Avg is : -0.24490779684850494
Kurtosis of the column Project_Grade is : -0.2379706935237178
Kurtosis of the column Progress_Percentage is : -0.17402158704955362
Kurtosis of the column Rewatch_Count is : 0.8293874576727518
Kurtosis of the column Payment_Amount is : -1.0156739316560142
Kurtosis of the column App_Usage_Percentage is : -0.4018101772870635
Kurtosis of the column Reminder_Emails_Clicked is : 0.606826976067909
Kurtosis of the column Support_Tickets_Raised is : 1.3845232097782572
Kurtosis of the column Satisfaction_Rating is : -0.16736313719962626

Student_ID

<bound method Series.unique of 0        STU100000
1        STU100001
2        STU100002
3        STU100003
4        STU100004
           ...    
99995    STU199995
99996    STU199996
99997    STU199997
99998    STU199998
99999    STU199999
Name: Student_ID, Length: 100000, dtype: object>

Number of unique values in the column Student_ID are : 100000

-----------------------------------------------------------------------------------------------
Name

<bound method Series.unique of 0           Vihaan Patel
1             Arjun Nair
2        Aditya Bhardwaj
3          Krishna Singh
4           Krishna Nair
              ...       
99995         Neha Singh
99996         Kavya Nair
99997          Neha Nair
99998       Pooja Sharma
99999        Rahul Patel
Name: Name, Length: 100000, dtype: object>

Number of unique values in the column Name are : 300

-----------------------------------------------------------------------------------------------
Gender

<bound method Series.unique of 0          Male
1        Female
2        Female
3        Female
4        Female
          ...  
99995    Female
99996    Female
99997      Male
99998    Female
99999      Male
Name: Gender, Length: 100000, dtype: object>

Number of unique values in the column Gender are : 3

-----------------------------------------------------------------------------------------------
Education_Level

<bound method Series.unique of 0         Diploma
1        Bachelor
2          Master
3         Diploma
4          Master
           ...   
99995    Bachelor
99996    Bachelor
99997      Master
99998    Bachelor
99999     Diploma
Name: Education_Level, Length: 100000, dtype: object>

Number of unique values in the column Education_Level are : 5

-----------------------------------------------------------------------------------------------
Employment_Status

<bound method Series.unique of 0              Student
1              Student
2              Student
3             Employed
4        Self-Employed
             ...      
99995          Student
99996    Self-Employed
99997         Employed
99998          Student
99999          Student
Name: Employment_Status, Length: 100000, dtype: object>

Number of unique values in the column Employment_Status are : 4

-----------------------------------------------------------------------------------------------
City

<bound method Series.unique of 0           Indore
1            Delhi
2          Chennai
3            Surat
4          Lucknow
           ...    
99995    Hyderabad
99996        Delhi
99997    Ahmedabad
99998    Ahmedabad
99999    Bengaluru
Name: City, Length: 100000, dtype: object>

Number of unique values in the column City are : 15

-----------------------------------------------------------------------------------------------
Device_Type

<bound method Series.unique of 0        Laptop
1        Laptop
2        Mobile
3        Mobile
4        Laptop
          ...  
99995    Mobile
99996    Mobile
99997    Laptop
99998    Mobile
99999    Laptop
Name: Device_Type, Length: 100000, dtype: object>

Number of unique values in the column Device_Type are : 3

-----------------------------------------------------------------------------------------------
Internet_Connection_Quality

<bound method Series.unique of 0        Medium
1           Low
2        Medium
3          High
4        Medium
          ...  
99995    Medium
99996      High
99997    Medium
99998      High
99999       Low
Name: Internet_Connection_Quality, Length: 100000, dtype: object>

Number of unique values in the column Internet_Connection_Quality are : 3

-----------------------------------------------------------------------------------------------
Course_ID

<bound method Series.unique of 0        C102
1        C106
2        C101
3        C105
4        C106
         ... 
99995    C104
99996    C104
99997    C107
99998    C104
99999    C105
Name: Course_ID, Length: 100000, dtype: object>

Number of unique values in the column Course_ID are : 8

-----------------------------------------------------------------------------------------------
Course_Name

<bound method Series.unique of 0           Data Analysis with Python
1                Machine Learning A-Z
2                       Python Basics
3           UI/UX Design Fundamentals
4                Machine Learning A-Z
                     ...             
99995    Digital Marketing Essentials
99996    Digital Marketing Essentials
99997     Statistics for Data Science
99998    Digital Marketing Essentials
99999       UI/UX Design Fundamentals
Name: Course_Name, Length: 100000, dtype: object>

Number of unique values in the column Course_Name are : 8

-----------------------------------------------------------------------------------------------
Category

<bound method Series.unique of 0        Programming
1        Programming
2        Programming
3             Design
4        Programming
            ...     
99995      Marketing
99996      Marketing
99997           Math
99998      Marketing
99999         Design
Name: Category, Length: 100000, dtype: object>

Number of unique values in the column Category are : 5

-----------------------------------------------------------------------------------------------
Course_Level

<bound method Series.unique of 0        Intermediate
1            Advanced
2            Beginner
3            Beginner
4            Advanced
             ...     
99995        Beginner
99996        Beginner
99997    Intermediate
99998        Beginner
99999        Beginner
Name: Course_Level, Length: 100000, dtype: object>

Number of unique values in the column Course_Level are : 3

-----------------------------------------------------------------------------------------------
Enrollment_Date

<bound method Series.unique of 0        01-06-2024
1        27-04-2025
2        20-01-2024
3        13-05-2025
4        19-12-2024
            ...    
99995    12-07-2024
99996    13-01-2024
99997    14-08-2024
99998    21-06-2025
99999    15-03-2024
Name: Enrollment_Date, Length: 100000, dtype: object>

Number of unique values in the column Enrollment_Date are : 721

-----------------------------------------------------------------------------------------------
Payment_Mode

<bound method Series.unique of 0        Scholarship
1        Credit Card
2         NetBanking
3                UPI
4         Debit Card
            ...     
99995            UPI
99996            UPI
99997            UPI
99998    Credit Card
99999    Credit Card
Name: Payment_Mode, Length: 100000, dtype: object>

Number of unique values in the column Payment_Mode are : 6

-----------------------------------------------------------------------------------------------
Fee_Paid

<bound method Series.unique of 0         No
1        Yes
2        Yes
3        Yes
4        Yes
        ... 
99995    Yes
99996    Yes
99997    Yes
99998    Yes
99999    Yes
Name: Fee_Paid, Length: 100000, dtype: object>

Number of unique values in the column Fee_Paid are : 2

-----------------------------------------------------------------------------------------------
Discount_Used

<bound method Series.unique of 0         No
1         No
2         No
3         No
4        Yes
        ... 
99995     No
99996     No
99997    Yes
99998     No
99999     No
Name: Discount_Used, Length: 100000, dtype: object>

Number of unique values in the column Discount_Used are : 2

-----------------------------------------------------------------------------------------------
Completed

<bound method Series.unique of 0            Completed
1        Not Completed
2            Completed
3            Completed
4            Completed
             ...      
99995        Completed
99996    Not Completed
99997    Not Completed
99998    Not Completed
99999    Not Completed
Name: Completed, Length: 100000, dtype: object>

Number of unique values in the column Completed are : 2

-----------------------------------------------------------------------------------------------

Student_ID

Student_ID
STU199960    1
STU199961    1
STU199962    1
STU199963    1
STU199964    1
            ..
STU100003    1
STU100004    1
STU100005    1
STU100006    1
STU100007    1
Name: count, Length: 100000, dtype: int64

-----------------------------------------------------------------------------------------------
Name

Name
Ritika Iyer      398
Arjun Joshi      387
Sakshi Iyer      384
Ritika Reddy     381
Sneha Patel      375
                ... 
Ritika Singh     300
Aarav Desai      299
Aarav Bose       297
Krishna Mehta    295
Sai Verma        280
Name: count, Length: 300, dtype: int64

-----------------------------------------------------------------------------------------------
Gender

Gender
Female    50187
Male      47819
Other      1994
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------
Education_Level

Education_Level
Bachelor      54956
Master        21859
HighSchool    10107
Diploma       10032
PhD            3046
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------
Employment_Status

Employment_Status
Employed         45091
Student          44929
Self-Employed     5067
Unemployed        4913
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------
City

City
Indore       6747
Delhi        6743
Mumbai       6731
Surat        6722
Nagpur       6707
Bengaluru    6707
Kolkata      6700
Lucknow      6675
Hyderabad    6652
Ahmedabad    6634
Bhopal       6625
Vadodara     6621
Jaipur       6613
Pune         6574
Chennai      6549
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------
Device_Type

Device_Type
Mobile    60021
Laptop    35018
Tablet     4961
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------
Internet_Connection_Quality

Internet_Connection_Quality
Medium    49985
High      35002
Low       15013
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------
Course_ID

Course_ID
C101    16807
C102    15465
C103    12702
C104    12686
C108    12631
C106    11198
C107    10025
C105     8486
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------
Course_Name

Course_Name
Python Basics                   16807
Data Analysis with Python       15465
Introduction to AI              12702
Digital Marketing Essentials    12686
Excel for Business              12631
Machine Learning A-Z            11198
Statistics for Data Science     10025
UI/UX Design Fundamentals        8486
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------
Category

Category
Programming    56172
Marketing      12686
Business       12631
Math           10025
Design          8486
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------
Course_Level

Course_Level
Beginner        50610
Intermediate    38192
Advanced        11198
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------
Enrollment_Date

Enrollment_Date
13-07-2025    177
10-05-2025    175
20-10-2024    174
21-11-2023    174
24-12-2023    172
             ... 
19-02-2025    109
30-05-2024    109
31-01-2024    109
07-04-2024    107
24-09-2024    102
Name: count, Length: 721, dtype: int64

-----------------------------------------------------------------------------------------------
Payment_Mode

Payment_Mode
UPI            30095
Free           19811
Credit Card    15129
Debit Card     14840
NetBanking     10086
Scholarship    10039
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------
Fee_Paid

Fee_Paid
Yes    70150
No     29850
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------
Discount_Used

Discount_Used
No     80058
Yes    19942
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------
Completed

Completed
Not Completed    50970
Completed        49030
Name: count, dtype: int64

-----------------------------------------------------------------------------------------------

['Age',
 'Course_Duration_Days',
 'Instructor_Rating',
 'Login_Frequency',
 'Average_Session_Duration_Min',
 'Video_Completion_Rate',
 'Discussion_Participation',
 'Time_Spent_Hours',
 'Days_Since_Last_Login',
 'Notifications_Checked',
 'Peer_Interaction_Score',
 'Assignments_Submitted',
 'Assignments_Missed',
 'Quiz_Attempts',
 'Quiz_Score_Avg',
 'Project_Grade',
 'Progress_Percentage',
 'Rewatch_Count',
 'Payment_Amount',
 'App_Usage_Percentage',
 'Reminder_Emails_Clicked',
 'Support_Tickets_Raised',
 'Satisfaction_Rating']

Hypothesis:
Ho: Student_ID and Completed are independent
Ha: Student_ID and Completed are dependent

Conclusion:
Fails to reject Ho
Ho: Student_ID and Completed are independent

------------------
Hypothesis:
Ho: Name and Completed are independent
Ha: Name and Completed are dependent

Conclusion:
Fails to reject Ho
Ho: Name and Completed are independent

------------------
Hypothesis:
Ho: Gender and Completed are independent
Ha: Gender and Completed are dependent

Conclusion:
Reject Ho
Ha: Gender and Completed are dependent

------------------
Hypothesis:
Ho: Education_Level and Completed are independent
Ha: Education_Level and Completed are dependent

Conclusion:
Reject Ho
Ha: Education_Level and Completed are dependent

------------------
Hypothesis:
Ho: Employment_Status and Completed are independent
Ha: Employment_Status and Completed are dependent

Conclusion:
Reject Ho
Ha: Employment_Status and Completed are dependent

------------------
Hypothesis:
Ho: City and Completed are independent
Ha: City and Completed are dependent

Conclusion:
Fails to reject Ho
Ho: City and Completed are independent

------------------
Hypothesis:
Ho: Device_Type and Completed are independent
Ha: Device_Type and Completed are dependent

Conclusion:
Reject Ho
Ha: Device_Type and Completed are dependent

------------------
Hypothesis:
Ho: Internet_Connection_Quality and Completed are independent
Ha: Internet_Connection_Quality and Completed are dependent

Conclusion:
Reject Ho
Ha: Internet_Connection_Quality and Completed are dependent

------------------
Hypothesis:
Ho: Course_ID and Completed are independent
Ha: Course_ID and Completed are dependent

Conclusion:
Fails to reject Ho
Ho: Course_ID and Completed are independent

------------------
Hypothesis:
Ho: Course_Name and Completed are independent
Ha: Course_Name and Completed are dependent

Conclusion:
Fails to reject Ho
Ho: Course_Name and Completed are independent

------------------
Hypothesis:
Ho: Category and Completed are independent
Ha: Category and Completed are dependent

Conclusion:
Fails to reject Ho
Ho: Category and Completed are independent

------------------
Hypothesis:
Ho: Course_Level and Completed are independent
Ha: Course_Level and Completed are dependent

Conclusion:
Fails to reject Ho
Ho: Course_Level and Completed are independent

------------------
Hypothesis:
Ho: Enrollment_Date and Completed are independent
Ha: Enrollment_Date and Completed are dependent

Conclusion:
Fails to reject Ho
Ho: Enrollment_Date and Completed are independent

------------------
Hypothesis:
Ho: Payment_Mode and Completed are independent
Ha: Payment_Mode and Completed are dependent

Conclusion:
Reject Ho
Ha: Payment_Mode and Completed are dependent

------------------
Hypothesis:
Ho: Fee_Paid and Completed are independent
Ha: Fee_Paid and Completed are dependent

Conclusion:
Reject Ho
Ha: Fee_Paid and Completed are dependent

------------------
Hypothesis:
Ho: Discount_Used and Completed are independent
Ha: Discount_Used and Completed are dependent

Conclusion:
Fails to reject Ho
Ho: Discount_Used and Completed are independent

------------------

50970

Hypothesis:

Ho : Mean of the Course_Duration_Days is SAME for 'Completed' and  'Not Completed'(μ(Completed) = μ(Not Completed))
Ha : Mean of the Course_Duration_Days is NOT SAME for 'Completed' and  'Not Completed'(μ(Completed) != μ(Not Completed))

CONCLUSION:Fails to reject Ho

Ho : Mean of the Course_Duration_Days is SAME for 'Completed' and  'Not Completed'

------------------
Hypothesis:

Ho : Mean of the Login_Frequency is SAME for 'Completed' and  'Not Completed'(μ(Completed) = μ(Not Completed))
Ha : Mean of the Login_Frequency is NOT SAME for 'Completed' and  'Not Completed'(μ(Completed) != μ(Not Completed))

CONCLUSION:Reject Ho
Ha : Mean of the Login_Frequency is NOT SAME for 'Completed' and  'Not Completed'

------------------
Hypothesis:

Ho : Mean of the Average_Session_Duration_Min is SAME for 'Completed' and  'Not Completed'(μ(Completed) = μ(Not Completed))
Ha : Mean of the Average_Session_Duration_Min is NOT SAME for 'Completed' and  'Not Completed'(μ(Completed) != μ(Not Completed))

CONCLUSION:Reject Ho
Ha : Mean of the Average_Session_Duration_Min is NOT SAME for 'Completed' and  'Not Completed'

------------------
Hypothesis:

Ho : Mean of the Video_Completion_Rate is SAME for 'Completed' and  'Not Completed'(μ(Completed) = μ(Not Completed))
Ha : Mean of the Video_Completion_Rate is NOT SAME for 'Completed' and  'Not Completed'(μ(Completed) != μ(Not Completed))

CONCLUSION:Reject Ho
Ha : Mean of the Video_Completion_Rate is NOT SAME for 'Completed' and  'Not Completed'

------------------
Hypothesis:

Ho : Mean of the Discussion_Participation is SAME for 'Completed' and  'Not Completed'(μ(Completed) = μ(Not Completed))
Ha : Mean of the Discussion_Participation is NOT SAME for 'Completed' and  'Not Completed'(μ(Completed) != μ(Not Completed))

CONCLUSION:Reject Ho
Ha : Mean of the Discussion_Participation is NOT SAME for 'Completed' and  'Not Completed'

------------------
Hypothesis:

Ho : Mean of the Time_Spent_Hours is SAME for 'Completed' and  'Not Completed'(μ(Completed) = μ(Not Completed))
Ha : Mean of the Time_Spent_Hours is NOT SAME for 'Completed' and  'Not Completed'(μ(Completed) != μ(Not Completed))

CONCLUSION:Reject Ho
Ha : Mean of the Time_Spent_Hours is NOT SAME for 'Completed' and  'Not Completed'

------------------
Hypothesis:

Ho : Mean of the Days_Since_Last_Login is SAME for 'Completed' and  'Not Completed'(μ(Completed) = μ(Not Completed))
Ha : Mean of the Days_Since_Last_Login is NOT SAME for 'Completed' and  'Not Completed'(μ(Completed) != μ(Not Completed))

CONCLUSION:Reject Ho
Ha : Mean of the Days_Since_Last_Login is NOT SAME for 'Completed' and  'Not Completed'

------------------
Hypothesis:

Ho : Mean of the Assignments_Submitted is SAME for 'Completed' and  'Not Completed'(μ(Completed) = μ(Not Completed))
Ha : Mean of the Assignments_Submitted is NOT SAME for 'Completed' and  'Not Completed'(μ(Completed) != μ(Not Completed))

CONCLUSION:Reject Ho
Ha : Mean of the Assignments_Submitted is NOT SAME for 'Completed' and  'Not Completed'

------------------
Hypothesis:

Ho : Mean of the Quiz_Score_Avg is SAME for 'Completed' and  'Not Completed'(μ(Completed) = μ(Not Completed))
Ha : Mean of the Quiz_Score_Avg is NOT SAME for 'Completed' and  'Not Completed'(μ(Completed) != μ(Not Completed))

CONCLUSION:Reject Ho
Ha : Mean of the Quiz_Score_Avg is NOT SAME for 'Completed' and  'Not Completed'

------------------
Hypothesis:

Ho : Mean of the Progress_Percentage is SAME for 'Completed' and  'Not Completed'(μ(Completed) = μ(Not Completed))
Ha : Mean of the Progress_Percentage is NOT SAME for 'Completed' and  'Not Completed'(μ(Completed) != μ(Not Completed))

CONCLUSION:Reject Ho
Ha : Mean of the Progress_Percentage is NOT SAME for 'Completed' and  'Not Completed'

------------------

Student_ID

['STU100000' 'STU100001' 'STU100002' ... 'STU199997' 'STU199998'
 'STU199999']

-------------------------------------------------------------------------

Name

['Vihaan Patel' 'Arjun Nair' 'Aditya Bhardwaj' 'Krishna Singh'
 'Krishna Nair' 'Rohan Reddy' 'Sai Nair' 'Krishna Desai' 'Vihaan Joshi'
 'Vivaan Nair' 'Aditya Gupta' 'Sneha Bhardwaj' 'Rohan Desai'
 'Vihaan Mehta' 'Sai Joshi' 'Sai Reddy' 'Rahul Singh' 'Rohan Shah'
 'Neha Sharma' 'Pooja Singh' 'Kavya Verma' 'Rahul Kumar' 'Priya Joshi'
 'Ritika Reddy' 'Arjun Iyer' 'Sakshi Sharma' 'Ritika Kumar' 'Vihaan Iyer'
 'Pooja Sharma' 'Aarav Nair' 'Sai Gupta' 'Ananya Shah' 'Arjun Joshi'
 'Aarav Bhardwaj' 'Sneha Bose' 'Vivaan Singh' 'Aditya Nair' 'Arjun Verma'
 'Sai Kumar' 'Krishna Bose' 'Sneha Reddy' 'Rohan Singh' 'Krishna Gupta'
 'Nikhil Reddy' 'Krishna Sharma' 'Isha Mehta' 'Isha Verma' 'Kavya Desai'
 'Vivaan Iyer' 'Rohan Verma' 'Priya Bose' 'Priya Kumar' 'Ananya Mehta'
 'Meera Verma' 'Nikhil Nair' 'Vivaan Patel' 'Sai Patel' 'Priya Sharma'
 'Sakshi Patel' 'Nikhil Bhardwaj' 'Krishna Iyer' 'Priya Verma'
 'Pooja Bose' 'Kavya Bhardwaj' 'Aditya Bose' 'Kavya Shah' 'Ritika Patel'
 'Vihaan Bose' 'Rahul Verma' 'Aditya Iyer' 'Aarav Joshi' 'Rahul Patel'
 'Arjun Gupta' 'Sneha Singh' 'Sneha Desai' 'Sakshi Nair' 'Sai Desai'
 'Aarav Verma' 'Rohan Kumar' 'Priya Shah' 'Krishna Shah' 'Pooja Kumar'
 'Sneha Iyer' 'Pooja Patel' 'Sai Bhardwaj' 'Isha Joshi' 'Sakshi Gupta'
 'Krishna Kumar' 'Ritika Bose' 'Krishna Joshi' 'Pooja Bhardwaj'
 'Ananya Gupta' 'Priya Nair' 'Isha Iyer' 'Sakshi Bhardwaj' 'Ananya Kumar'
 'Sakshi Iyer' 'Aditya Kumar' 'Ananya Singh' 'Kavya Mehta' 'Priya Reddy'
 'Aarav Bose' 'Isha Singh' 'Rahul Bhardwaj' 'Sakshi Shah' 'Rahul Reddy'
 'Kavya Kumar' 'Neha Desai' 'Aditya Verma' 'Pooja Gupta' 'Aarav Shah'
 'Meera Patel' 'Isha Gupta' 'Aarav Mehta' 'Sai Shah' 'Aarav Sharma'
 'Kavya Joshi' 'Rohan Iyer' 'Vivaan Desai' 'Vihaan Bhardwaj' 'Sneha Verma'
 'Nikhil Iyer' 'Rohan Sharma' 'Sakshi Desai' 'Vivaan Joshi' 'Meera Sharma'
 'Kavya Nair' 'Neha Bhardwaj' 'Pooja Shah' 'Neha Bose' 'Meera Shah'
 'Neha Gupta' 'Aditya Singh' 'Rohan Nair' 'Rahul Joshi' 'Sai Singh'
 'Nikhil Desai' 'Vivaan Gupta' 'Meera Reddy' 'Neha Shah' 'Vivaan Verma'
 'Isha Kumar' 'Vihaan Desai' 'Meera Kumar' 'Kavya Sharma' 'Vihaan Gupta'
 'Arjun Sharma' 'Sakshi Kumar' 'Meera Gupta' 'Aditya Reddy' 'Vihaan Shah'
 'Vivaan Reddy' 'Kavya Bose' 'Vihaan Reddy' 'Meera Bose' 'Isha Shah'
 'Kavya Gupta' 'Ritika Nair' 'Rahul Mehta' 'Rohan Bhardwaj' 'Priya Iyer'
 'Meera Desai' 'Sneha Mehta' 'Kavya Iyer' 'Nikhil Kumar' 'Neha Reddy'
 'Vivaan Bose' 'Krishna Bhardwaj' 'Vivaan Kumar' 'Sneha Nair' 'Pooja Nair'
 'Rahul Bose' 'Ananya Nair' 'Priya Patel' 'Vihaan Verma' 'Arjun Mehta'
 'Kavya Reddy' 'Sneha Gupta' 'Vivaan Mehta' 'Arjun Desai' 'Pooja Desai'
 'Aarav Kumar' 'Nikhil Joshi' 'Aditya Joshi' 'Priya Bhardwaj'
 'Sakshi Mehta' 'Rahul Nair' 'Rohan Mehta' 'Isha Nair' 'Neha Joshi'
 'Aditya Sharma' 'Vihaan Nair' 'Pooja Iyer' 'Krishna Verma' 'Sneha Shah'
 'Nikhil Singh' 'Sakshi Bose' 'Pooja Joshi' 'Isha Reddy' 'Sakshi Reddy'
 'Ananya Joshi' 'Sai Verma' 'Ritika Gupta' 'Aditya Mehta' 'Aarav Reddy'
 'Nikhil Mehta' 'Sneha Sharma' 'Isha Bhardwaj' 'Arjun Singh'
 'Sakshi Joshi' 'Arjun Reddy' 'Ananya Desai' 'Ritika Sharma'
 'Nikhil Gupta' 'Sneha Joshi' 'Arjun Bose' 'Sneha Patel' 'Ritika Desai'
 'Arjun Bhardwaj' 'Rohan Gupta' 'Ritika Mehta' 'Arjun Shah' 'Vihaan Kumar'
 'Meera Nair' 'Neha Singh' 'Ananya Sharma' 'Rahul Gupta' 'Arjun Patel'
 'Meera Iyer' 'Kavya Patel' 'Pooja Verma' 'Sakshi Verma' 'Ritika Joshi'
 'Neha Nair' 'Isha Desai' 'Nikhil Verma' 'Ritika Verma' 'Rohan Joshi'
 'Ritika Iyer' 'Aditya Patel' 'Ananya Reddy' 'Rahul Sharma'
 'Meera Bhardwaj' 'Priya Gupta' 'Pooja Mehta' 'Pooja Reddy' 'Ananya Patel'
 'Neha Verma' 'Aarav Gupta' 'Sai Mehta' 'Priya Desai' 'Neha Iyer'
 'Sakshi Singh' 'Ritika Singh' 'Aarav Desai' 'Aarav Singh' 'Meera Singh'
 'Vivaan Shah' 'Ananya Bose' 'Rahul Iyer' 'Vivaan Bhardwaj' 'Sai Sharma'
 'Ananya Iyer' 'Vivaan Sharma' 'Nikhil Shah' 'Sai Bose' 'Aditya Desai'
 'Krishna Reddy' 'Nikhil Sharma' 'Vihaan Singh' 'Meera Mehta' 'Rahul Shah'
 'Ananya Bhardwaj' 'Ananya Verma' 'Vihaan Sharma' 'Krishna Patel'
 'Rahul Desai' 'Isha Patel' 'Nikhil Patel' 'Ritika Bhardwaj' 'Neha Mehta'
 'Meera Joshi' 'Isha Bose' 'Priya Singh' 'Rohan Bose' 'Ritika Shah'
 'Aarav Iyer' 'Krishna Mehta' 'Rohan Patel' 'Priya Mehta' 'Sai Iyer'
 'Aarav Patel' 'Arjun Kumar' 'Sneha Kumar' 'Neha Kumar' 'Aditya Shah'
 'Kavya Singh' 'Nikhil Bose' 'Neha Patel' 'Isha Sharma']

-------------------------------------------------------------------------

Gender

['Male' 'Female' 'Other']

-------------------------------------------------------------------------

Education_Level

['Diploma' 'Bachelor' 'Master' 'HighSchool' 'PhD']

-------------------------------------------------------------------------

Employment_Status

['Student' 'Employed' 'Self-Employed' 'Unemployed']

-------------------------------------------------------------------------

City

['Indore' 'Delhi' 'Chennai' 'Surat' 'Lucknow' 'Jaipur' 'Hyderabad'
 'Nagpur' 'Kolkata' 'Ahmedabad' 'Pune' 'Mumbai' 'Bengaluru' 'Bhopal'
 'Vadodara']

-------------------------------------------------------------------------

Device_Type

['Laptop' 'Mobile' 'Tablet']

-------------------------------------------------------------------------

Internet_Connection_Quality

['Medium' 'Low' 'High']

-------------------------------------------------------------------------

Course_ID

['C102' 'C106' 'C101' 'C105' 'C103' 'C104' 'C107' 'C108']

-------------------------------------------------------------------------

Course_Name

['Data Analysis with Python' 'Machine Learning A-Z' 'Python Basics'
 'UI/UX Design Fundamentals' 'Introduction to AI'
 'Digital Marketing Essentials' 'Statistics for Data Science'
 'Excel for Business']

-------------------------------------------------------------------------

Category

['Programming' 'Design' 'Marketing' 'Math' 'Business']

-------------------------------------------------------------------------

Course_Level

['Intermediate' 'Advanced' 'Beginner']

-------------------------------------------------------------------------

Enrollment_Date

['01-06-2024' '27-04-2025' '20-01-2024' '13-05-2025' '19-12-2024'
 '23-10-2023' '24-03-2024' '09-11-2024' '13-07-2024' '07-11-2024'
 '11-07-2025' '02-12-2023' '11-06-2024' '24-10-2024' '16-07-2024'
 '23-06-2024' '02-08-2025' '13-12-2023' '30-11-2024' '19-05-2025'
 '05-08-2025' '05-06-2025' '06-03-2024' '06-02-2024' '22-12-2024'
 '07-02-2025' '28-10-2023' '23-01-2025' '12-03-2025' '18-07-2025'
 '07-05-2024' '12-04-2025' '08-02-2024' '18-06-2024' '05-01-2024'
 '14-04-2025' '01-05-2025' '23-12-2024' '22-09-2025' '03-09-2025'
 '17-02-2024' '10-06-2025' '28-12-2023' '19-09-2024' '25-02-2025'
 '23-05-2025' '06-12-2024' '13-10-2024' '11-12-2023' '05-02-2024'
 '08-03-2024' '02-01-2024' '25-01-2024' '22-05-2024' '19-10-2023'
 '24-01-2025' '08-05-2025' '15-12-2023' '02-09-2024' '24-04-2024'
 '13-06-2024' '31-05-2025' '20-08-2025' '10-07-2024' '14-07-2024'
 '27-01-2025' '26-10-2023' '31-12-2023' '08-10-2024' '26-03-2024'
 '04-07-2025' '18-08-2024' '03-02-2024' '19-05-2024' '08-12-2023'
 '01-12-2023' '11-07-2024' '27-12-2024' '23-09-2025' '04-06-2025'
 '27-08-2024' '26-11-2023' '30-01-2024' '15-06-2024' '12-12-2024'
 '11-12-2024' '04-02-2024' '31-05-2024' '24-08-2024' '28-11-2024'
 '28-07-2024' '18-04-2025' '18-06-2025' '05-01-2025' '29-05-2025'
 '18-10-2023' '02-09-2025' '27-08-2025' '21-02-2024' '15-09-2024'
 '30-04-2024' '12-04-2024' '17-10-2023' '01-06-2025' '11-01-2025'
 '07-04-2024' '12-07-2025' '11-11-2023' '30-12-2023' '03-05-2024'
 '18-08-2025' '28-09-2025' '18-02-2024' '12-01-2025' '26-12-2024'
 '14-02-2025' '30-07-2025' '19-07-2025' '04-08-2024' '22-04-2025'
 '26-09-2025' '07-12-2024' '15-05-2024' '19-11-2024' '25-06-2025'
 '04-12-2024' '15-07-2025' '16-05-2025' '31-01-2025' '20-05-2024'
 '08-11-2024' '08-01-2024' '09-10-2024' '14-08-2025' '08-05-2024'
 '12-02-2025' '05-07-2024' '11-02-2024' '31-08-2024' '26-11-2024'
 '20-07-2025' '16-09-2025' '18-05-2025' '30-10-2024' '20-12-2024'
 '28-07-2025' '03-04-2025' '23-02-2025' '17-07-2024' '13-01-2024'
 '03-02-2025' '15-11-2023' '21-05-2024' '29-04-2024' '07-05-2025'
 '02-03-2025' '30-05-2024' '03-06-2025' '25-12-2024' '20-07-2024'
 '01-05-2024' '26-07-2024' '27-01-2024' '21-08-2024' '23-02-2024'
 '03-08-2024' '22-12-2023' '18-09-2025' '23-08-2024' '16-12-2024'
 '04-04-2025' '02-07-2025' '01-01-2024' '16-06-2025' '01-08-2024'
 '15-07-2024' '26-08-2024' '07-08-2024' '02-10-2025' '07-01-2025'
 '11-09-2024' '06-09-2025' '04-09-2025' '13-04-2024' '27-07-2024'
 '03-03-2025' '09-09-2024' '02-05-2025' '12-09-2024' '30-03-2025'
 '06-08-2024' '30-08-2024' '03-06-2024' '22-03-2024' '22-07-2024'
 '22-06-2024' '21-06-2025' '27-12-2023' '29-09-2024' '17-08-2024'
 '17-03-2024' '04-05-2025' '27-04-2024' '10-12-2024' '10-11-2024'
 '20-03-2024' '12-06-2025' '09-01-2025' '12-03-2024' '25-09-2025'
 '27-02-2025' '24-07-2025' '18-10-2024' '15-09-2025' '10-06-2024'
 '29-08-2025' '04-10-2024' '13-11-2024' '21-08-2025' '05-08-2024'
 '29-07-2025' '22-08-2025' '08-08-2025' '14-08-2024' '29-02-2024'
 '21-02-2025' '04-06-2024' '17-01-2024' '06-06-2025' '20-12-2023'
 '08-12-2024' '19-04-2025' '29-09-2025' '23-03-2024' '08-07-2025'
 '26-05-2024' '26-01-2024' '19-02-2024' '01-08-2025' '06-05-2025'
 '27-02-2024' '23-08-2025' '16-06-2024' '09-12-2023' '21-12-2023'
 '15-06-2025' '07-07-2025' '17-11-2023' '13-09-2024' '17-03-2025'
 '21-01-2025' '01-09-2024' '13-02-2025' '01-01-2025' '14-05-2025'
 '28-08-2025' '19-03-2025' '12-11-2023' '28-08-2024' '11-03-2024'
 '26-07-2025' '05-11-2024' '24-03-2025' '10-09-2025' '13-05-2024'
 '04-07-2024' '30-12-2024' '08-06-2024' '25-11-2024' '06-04-2024'
 '22-03-2025' '07-08-2025' '06-05-2024' '17-05-2025' '04-09-2024'
 '11-10-2024' '24-11-2023' '01-02-2025' '28-11-2023' '26-04-2024'
 '19-12-2023' '02-10-2024' '26-06-2024' '27-03-2024' '19-08-2025'
 '11-01-2024' '17-09-2024' '17-09-2025' '20-03-2025' '23-05-2024'
 '05-03-2024' '28-01-2024' '08-04-2025' '10-07-2025' '19-04-2024'
 '18-01-2025' '08-02-2025' '10-01-2025' '14-03-2025' '27-06-2025'
 '20-02-2025' '28-03-2025' '06-01-2025' '02-11-2024' '14-11-2023'
 '12-08-2025' '05-11-2023' '01-07-2025' '11-03-2025' '27-05-2025'
 '25-04-2024' '07-04-2025' '16-10-2024' '21-07-2025' '21-09-2024'
 '27-11-2024' '09-08-2025' '04-12-2023' '15-11-2024' '27-10-2024'
 '24-12-2024' '18-04-2024' '16-09-2024' '29-11-2023' '14-12-2023'
 '25-03-2025' '19-11-2023' '10-04-2024' '08-09-2024' '09-08-2024'
 '03-10-2025' '05-03-2025' '06-02-2025' '23-12-2023' '05-02-2025'
 '03-05-2025' '02-01-2025' '24-07-2024' '02-06-2025' '08-08-2024'
 '17-12-2023' '12-05-2024' '07-09-2024' '27-03-2025' '07-12-2023'
 '27-06-2024' '09-11-2023' '19-06-2024' '31-07-2024' '25-10-2024'
 '22-05-2025' '29-07-2024' '24-01-2024' '10-05-2025' '21-03-2025'
 '05-07-2025' '03-11-2023' '20-09-2025' '18-11-2023' '10-02-2025'
 '28-04-2025' '13-09-2025' '25-06-2024' '16-08-2024' '28-02-2025'
 '23-07-2024' '14-03-2024' '18-02-2025' '08-06-2025' '13-12-2024'
 '15-08-2025' '16-01-2024' '23-11-2023' '25-04-2025' '16-03-2025'
 '20-05-2025' '05-12-2024' '01-09-2025' '17-02-2025' '21-10-2024'
 '29-04-2025' '02-04-2024' '12-09-2025' '20-10-2023' '03-10-2024'
 '26-12-2023' '03-03-2024' '06-11-2024' '21-04-2025' '16-04-2024'
 '02-02-2025' '05-12-2023' '12-06-2024' '28-05-2025' '18-12-2023'
 '19-01-2025' '09-06-2025' '28-04-2024' '21-06-2024' '19-10-2024'
 '04-08-2025' '22-04-2024' '23-07-2025' '27-05-2024' '31-07-2025'
 '02-11-2023' '16-08-2025' '09-04-2024' '20-09-2024' '16-05-2024'
 '17-06-2025' '04-11-2024' '27-07-2025' '19-07-2024' '20-06-2025'
 '11-05-2025' '04-03-2025' '21-12-2024' '30-06-2024' '09-07-2025'
 '18-09-2024' '13-08-2025' '27-09-2025' '30-07-2024' '18-05-2024'
 '22-08-2024' '24-09-2025' '09-09-2025' '11-09-2025' '12-01-2024'
 '22-02-2025' '17-11-2024' '11-04-2024' '29-05-2024' '07-03-2025'
 '29-01-2024' '08-01-2025' '28-12-2024' '19-06-2025' '11-08-2024'
 '26-03-2025' '16-12-2023' '18-07-2024' '28-10-2024' '26-09-2024'
 '24-02-2025' '03-12-2023' '26-01-2025' '31-10-2024' '22-01-2024'
 '25-10-2023' '01-03-2024' '20-02-2024' '29-12-2024' '30-06-2025'
 '29-10-2023' '21-05-2025' '15-12-2024' '29-08-2024' '17-06-2024'
 '12-02-2024' '04-10-2025' '04-04-2024' '12-12-2023' '12-11-2024'
 '22-11-2023' '16-02-2024' '04-02-2025' '10-08-2024' '04-05-2024'
 '10-03-2024' '01-04-2025' '23-11-2024' '25-08-2025' '03-09-2024'
 '06-09-2024' '11-05-2024' '15-10-2024' '09-07-2024' '13-01-2025'
 '26-05-2025' '16-11-2023' '28-06-2024' '30-09-2025' '06-11-2023'
 '06-07-2025' '15-04-2024' '11-08-2025' '11-06-2025' '13-08-2024'
 '08-03-2025' '06-10-2024' '15-01-2024' '30-05-2025' '20-11-2023'
 '01-02-2024' '29-03-2024' '27-09-2024' '03-01-2025' '23-01-2024'
 '14-01-2024' '19-02-2025' '12-07-2024' '30-08-2025' '23-04-2025'
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 '22-06-2025' '25-02-2024' '21-04-2024' '14-02-2024' '07-01-2024'
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 '07-07-2024' '07-06-2025' '29-06-2025' '25-03-2024' '09-03-2024'
 '01-07-2024' '02-05-2024' '26-04-2025' '29-03-2025' '08-11-2023'
 '02-08-2024' '23-06-2025' '20-04-2024' '13-04-2025' '30-11-2023'
 '16-02-2025' '24-10-2023' '02-07-2024' '17-04-2024' '18-01-2024'
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 '05-06-2024' '04-03-2024' '30-04-2025' '06-01-2024' '09-02-2025'
 '14-11-2024' '24-04-2025' '07-10-2024' '23-04-2024' '20-04-2025'
 '01-03-2025' '06-12-2023' '02-02-2024' '25-07-2024' '04-01-2024'
 '23-09-2024' '22-11-2024' '16-03-2024' '13-03-2025' '05-04-2025'
 '31-10-2023' '17-12-2024' '09-05-2025' '15-01-2025' '22-10-2023'
 '08-07-2024' '26-02-2024' '25-07-2025' '03-11-2024' '01-10-2025'
 '06-08-2025' '10-10-2024' '26-02-2025' '07-03-2024' '26-08-2025'
 '13-06-2025' '20-08-2024' '11-04-2025' '31-12-2024' '20-11-2024'
 '21-09-2025' '03-07-2025' '14-01-2025' '06-06-2024' '25-05-2024'
 '05-05-2025' '06-10-2025' '10-11-2023' '27-11-2023' '03-01-2024'
 '31-03-2024' '06-04-2025' '21-01-2024' '19-09-2025' '08-09-2025'
 '29-11-2024' '09-06-2024' '25-12-2023' '11-11-2024' '18-11-2024'
 '30-09-2024' '24-06-2024' '07-11-2023' '14-06-2024' '20-01-2025'
 '28-01-2025' '15-03-2025' '24-02-2024' '19-03-2024' '02-12-2024'
 '19-08-2024' '26-06-2025' '07-09-2025' '21-11-2024' '30-03-2024'
 '27-10-2023' '01-12-2024' '09-04-2025' '28-06-2025' '22-02-2024'
 '02-06-2024' '14-06-2025' '22-09-2024' '17-10-2024' '24-09-2024'
 '14-04-2024' '07-02-2024' '09-01-2024' '10-05-2024' '03-04-2024'
 '03-07-2024' '07-06-2024' '12-05-2025' '28-05-2024' '12-10-2024'
 '23-10-2024' '14-09-2025' '14-12-2024' '05-10-2024' '17-05-2024'
 '15-03-2024' '14-10-2024' '29-06-2024' '31-08-2025' '26-10-2024'
 '24-05-2024' '29-10-2024' '15-02-2024' '18-12-2024' '11-02-2025'
 '13-11-2023' '16-11-2024' '15-08-2024' '13-02-2024' '02-03-2024'
 '12-08-2024' '10-04-2025' '14-05-2024' '05-04-2024' '05-05-2024'
 '15-05-2025' '25-01-2025' '21-03-2024' '17-01-2025' '29-01-2025'
 '02-04-2025']

-------------------------------------------------------------------------

Payment_Mode

['Scholarship' 'Credit Card' 'NetBanking' 'UPI' 'Debit Card' 'Free']

-------------------------------------------------------------------------

Fee_Paid

['No' 'Yes']

-------------------------------------------------------------------------

Discount_Used

['No' 'Yes']

-------------------------------------------------------------------------

Completed

['Completed' 'Not Completed']

-------------------------------------------------------------------------

['Age',
 'Course_Duration_Days',
 'Instructor_Rating',
 'Login_Frequency',
 'Average_Session_Duration_Min',
 'Video_Completion_Rate',
 'Discussion_Participation',
 'Time_Spent_Hours',
 'Days_Since_Last_Login',
 'Notifications_Checked',
 'Peer_Interaction_Score',
 'Assignments_Submitted',
 'Assignments_Missed',
 'Quiz_Attempts',
 'Quiz_Score_Avg',
 'Project_Grade',
 'Progress_Percentage',
 'Rewatch_Count',
 'Payment_Amount',
 'App_Usage_Percentage',
 'Reminder_Emails_Clicked',
 'Support_Tickets_Raised',
 'Satisfaction_Rating']

(100000, 36)

array(['Diploma', 'Bachelor', 'Master', 'HighSchool', 'PhD'], dtype=object)

array([3., 2., 1., 4., 0.])

array(['Medium', 'Low', 'High'], dtype=object)

array([1., 2., 0.])

array(['Intermediate', 'Advanced', 'Beginner'], dtype=object)

array([1., 0., 2.])

Optimization terminated successfully.
         Current function value: 0.657366
         Iterations 5
                           Logit Regression Results                           
==============================================================================
Dep. Variable:              Completed   No. Observations:                80000
Model:                          Logit   Df Residuals:                    79964
Method:                           MLE   Df Model:                           35
Date:                Mon, 05 Jan 2026   Pseudo R-squ.:                 0.05132
Time:                        11:13:24   Log-Likelihood:                -52589.
converged:                       True   LL-Null:                       -55434.
Covariance Type:            nonrobust   LLR p-value:                     0.000
================================================================================================
                                   coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------------------------
const                            0.1838      0.082      2.244      0.025       0.023       0.344
Gender                           0.0247      0.014      1.815      0.070      -0.002       0.051
Age                              0.0065      0.011      0.592      0.554      -0.015       0.028
Education_Level                  0.0048      0.008      0.600      0.548      -0.011       0.020
Employment_Status                0.0080      0.007      1.119      0.263      -0.006       0.022
City                             0.0010      0.002      0.585      0.558      -0.002       0.004
Device_Type                     -0.0050      0.013     -0.380      0.704      -0.031       0.021
Internet_Connection_Quality      0.0068      0.011      0.626      0.531      -0.015       0.028
Course_Name                     -0.0002      0.004     -0.056      0.956      -0.008       0.007
Category                        -0.0022      0.010     -0.210      0.834      -0.023       0.018
Course_Level                    -0.0141      0.034     -0.412      0.681      -0.081       0.053
Course_Duration_Days             0.0272      0.044      0.623      0.533      -0.058       0.113
Instructor_Rating                0.0198      0.023      0.860      0.390      -0.025       0.065
Login_Frequency                  0.0136      0.013      1.088      0.277      -0.011       0.038
Average_Session_Duration_Min    -0.0052      0.010     -0.511      0.610      -0.025       0.015
Video_Completion_Rate           -0.2905      0.045     -6.407      0.000      -0.379      -0.202
Discussion_Participation        -0.0049      0.010     -0.495      0.620      -0.024       0.014
Time_Spent_Hours                -0.2717      0.012    -23.605      0.000      -0.294      -0.249
Days_Since_Last_Login            0.0759      0.009      8.879      0.000       0.059       0.093
Notifications_Checked           -0.0034      0.009     -0.360      0.719      -0.022       0.015
Peer_Interaction_Score          -0.0100      0.011     -0.947      0.344      -0.031       0.011
Assignments_Submitted           -0.0144      0.034     -0.427      0.669      -0.081       0.052
Assignments_Missed               0.0332      0.045      0.742      0.458      -0.054       0.121
Quiz_Attempts                    0.0246      0.011      2.212      0.027       0.003       0.046
Quiz_Score_Avg                  -0.1561      0.010    -15.039      0.000      -0.176      -0.136
Project_Grade                    0.0050      0.011      0.443      0.658      -0.017       0.027
Progress_Percentage             -0.3959      0.074     -5.318      0.000      -0.542      -0.250
Rewatch_Count                   -0.0087      0.010     -0.867      0.386      -0.028       0.011
Payment_Mode                    -0.0035      0.004     -0.865      0.387      -0.011       0.004
Fee_Paid                        -0.2111      0.051     -4.154      0.000      -0.311      -0.111
Discount_Used                   -0.0325      0.019     -1.692      0.091      -0.070       0.005
Payment_Amount                  -0.1705      0.041     -4.128      0.000      -0.252      -0.090
App_Usage_Percentage            -0.0284      0.010     -2.722      0.006      -0.049      -0.008
Reminder_Emails_Clicked          0.0087      0.009      0.923      0.356      -0.010       0.027
Support_Tickets_Raised          -0.0047      0.008     -0.602      0.547      -0.020       0.011
Satisfaction_Rating              0.0179      0.012      1.522      0.128      -0.005       0.041
================================================================================================

75721    0.423102
80184    0.329525
19864    0.632966
76699    0.314073
92991    0.310832
           ...   
32595    0.461341
29313    0.310339
37862    0.528044
53421    0.460315
42410    0.515216
Length: 20000, dtype: float64

[0, 0, 1, 0, 0]

              precision    recall  f1-score   support

           0       0.60      0.58      0.59      9862
           1       0.60      0.62      0.61     10138

    accuracy                           0.60     20000
   macro avg       0.60      0.60      0.60     20000
weighted avg       0.60      0.60      0.60     20000

                  Name  Accuracy  Precision  Recall  F1 Score
0  Logit-Model-1(Test)     0.602      0.604    0.62     0.612

75220    0.589794
48955    0.638164
44966    0.431316
13568    0.631952
92727    0.547524
dtype: float64

[1, 1, 0, 1, 1]

              precision    recall  f1-score   support

           0       0.60      0.59      0.59     39168
           1       0.61      0.63      0.62     40832

    accuracy                           0.61     80000
   macro avg       0.61      0.61      0.61     80000
weighted avg       0.61      0.61      0.61     80000

                   Name  Accuracy  Precision  Recall  F1 Score
0   Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1  Logit-Model-1(Train)     0.608      0.614   0.629     0.621

Completed
1    50970
0    49030
Name: count, dtype: int64

KNeighborsClassifier()

KNeighborsClassifier()

  File "C:\Users\harsh\anaconda3\Lib\site-packages\joblib\externals\loky\backend\context.py", line 257, in _count_physical_cores
    cpu_info = subprocess.run(
        "wmic CPU Get NumberOfCores /Format:csv".split(),
        capture_output=True,
        text=True,
    )
  File "C:\Users\harsh\anaconda3\Lib\subprocess.py", line 554, in run
    with Popen(*popenargs, **kwargs) as process:
         ~~~~~^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\harsh\anaconda3\Lib\subprocess.py", line 1039, in __init__
    self._execute_child(args, executable, preexec_fn, close_fds,
    ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                        pass_fds, cwd, env,
                        ^^^^^^^^^^^^^^^^^^^
    ...<5 lines>...
                        gid, gids, uid, umask,
                        ^^^^^^^^^^^^^^^^^^^^^^
                        start_new_session, process_group)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\harsh\anaconda3\Lib\subprocess.py", line 1554, in _execute_child
    hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
                       ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^
                             # no special security
                             ^^^^^^^^^^^^^^^^^^^^^
    ...<4 lines>...
                             cwd,
                             ^^^^
                             startupinfo)
                             ^^^^^^^^^^^^

array([1, 1, 1, 1, 0])

              precision    recall  f1-score   support

           0       0.53      0.52      0.53      9862
           1       0.55      0.56      0.55     10138

    accuracy                           0.54     20000
   macro avg       0.54      0.54      0.54     20000
weighted avg       0.54      0.54      0.54     20000

                   Name  Accuracy  Precision  Recall  F1 Score
0   Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1  Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2     KNN-Model-2(Test)     0.540      0.545   0.559     0.552

array([1, 1, 0, 0, 0])

              precision    recall  f1-score   support

           0       0.70      0.70      0.70     39168
           1       0.71      0.72      0.72     40832

    accuracy                           0.71     80000
   macro avg       0.71      0.71      0.71     80000
weighted avg       0.71      0.71      0.71     80000

                   Name  Accuracy  Precision  Recall  F1 Score
0   Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1  Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2     KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3    KNN-Model-2(Train)     0.709      0.714   0.718     0.716

array([[0.2, 0.8],
       [0.4, 0.6],
       [0.4, 0.6],
       [0.4, 0.6],
       [1. , 0. ]])

array([0.8, 0.6, 0.6, ..., 0.8, 0.8, 0.4])

GaussianNB()

GaussianNB()

array([1, 0, 1, 0, 0])

              precision    recall  f1-score   support

           0       0.59      0.60      0.60      9862
           1       0.61      0.59      0.60     10138

    accuracy                           0.60     20000
   macro avg       0.60      0.60      0.60     20000
weighted avg       0.60      0.60      0.60     20000

                       Name  Accuracy  Precision  Recall  F1 Score
0       Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1      Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2         KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3        KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4  GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598

array([1, 1, 0, 1, 1])

              precision    recall  f1-score   support

           0       0.59      0.60      0.60     39168
           1       0.61      0.60      0.60     40832

    accuracy                           0.60     80000
   macro avg       0.60      0.60      0.60     80000
weighted avg       0.60      0.60      0.60     80000

                        Name  Accuracy  Precision  Recall  F1 Score
0        Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1       Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2          KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3         KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4   GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598
5  GaussianNB-Model-3(Train)     0.599      0.610   0.597     0.603

array([[0.47449917, 0.52550083],
       [0.87242511, 0.12757489],
       [0.48429252, 0.51570748],
       [0.87412849, 0.12587151],
       [0.89743128, 0.10256872]])

array([0.52550083, 0.12757489, 0.51570748, ..., 0.5322441 , 0.30681517,
       0.43249505])

DecisionTreeClassifier(random_state=42)

DecisionTreeClassifier(random_state=42)

array([1, 0, 1, 0, 0])

              precision    recall  f1-score   support

           0       0.52      0.52      0.52      9862
           1       0.53      0.53      0.53     10138

    accuracy                           0.52     20000
   macro avg       0.52      0.52      0.52     20000
weighted avg       0.52      0.52      0.52     20000

                          Name  Accuracy  Precision  Recall  F1 Score
0          Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1         Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2            KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3           KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4     GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598
5    GaussianNB-Model-3(Train)     0.599      0.610   0.597     0.603
6  Decision Tree-Model-4(Test)     0.523      0.530   0.525     0.528

array([1, 1, 0, 1, 1])

              precision    recall  f1-score   support

           0       1.00      1.00      1.00     39168
           1       1.00      1.00      1.00     40832

    accuracy                           1.00     80000
   macro avg       1.00      1.00      1.00     80000
weighted avg       1.00      1.00      1.00     80000

                           Name  Accuracy  Precision  Recall  F1 Score
0           Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1          Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2             KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3            KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4      GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598
5     GaussianNB-Model-3(Train)     0.599      0.610   0.597     0.603
6   Decision Tree-Model-4(Test)     0.523      0.530   0.525     0.528
7  Decision Tree-Model-4(Train)     1.000      1.000   1.000     1.000

array([[0., 1.],
       [1., 0.],
       [0., 1.],
       [1., 0.],
       [1., 0.]])

array([1., 0., 1., ..., 1., 1., 1.])

array([[0.2, 0.8],
       [0.4, 0.6],
       [0.4, 0.6],
       [0.4, 0.6],
       [1. , 0. ]])

DecisionTreeClassifier(random_state=42)

DecisionTreeClassifier(random_state=42)

array([0, 0, 1, 0, 0])

              precision    recall  f1-score   support

           0       0.52      0.53      0.53      9862
           1       0.54      0.53      0.53     10138

    accuracy                           0.53     20000
   macro avg       0.53      0.53      0.53     20000
weighted avg       0.53      0.53      0.53     20000

                              Name  Accuracy  Precision  Recall  F1 Score
0              Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1             Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2                KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3               KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4         GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598
5        GaussianNB-Model-3(Train)     0.599      0.610   0.597     0.603
6      Decision Tree-Model-4(Test)     0.523      0.530   0.525     0.528
7     Decision Tree-Model-4(Train)     1.000      1.000   1.000     1.000
8  Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531     0.534

array([1, 1, 0, 1, 1])

              precision    recall  f1-score   support

           0       1.00      1.00      1.00     39168
           1       1.00      1.00      1.00     40832

    accuracy                           1.00     80000
   macro avg       1.00      1.00      1.00     80000
weighted avg       1.00      1.00      1.00     80000

                               Name  Accuracy  Precision  Recall  F1 Score
0               Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1              Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2                 KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3                KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4          GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598
5         GaussianNB-Model-3(Train)     0.599      0.610   0.597     0.603
6       Decision Tree-Model-4(Test)     0.523      0.530   0.525     0.528
7      Decision Tree-Model-4(Train)     1.000      1.000   1.000     1.000
8   Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531     0.534
9  Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000     1.000

array([[1., 0.],
       [1., 0.],
       [0., 1.],
       [1., 0.],
       [1., 0.]])

array([0., 0., 1., ..., 0., 1., 1.])

{'criterion': 'entropy',
 'max_depth': 5,
 'max_features': 'sqrt',
 'max_leaf_nodes': 8,
 'min_samples_leaf': 1,
 'min_samples_split': 2}

array([0, 1, 1, 0, 0])

              precision    recall  f1-score   support

           0       0.53      0.65      0.58      9862
           1       0.56      0.44      0.50     10138

    accuracy                           0.54     20000
   macro avg       0.55      0.55      0.54     20000
weighted avg       0.55      0.54      0.54     20000

                                  Name  Accuracy  Precision  Recall  F1 Score
0                  Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1                 Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2                    KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3                   KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4             GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598
5            GaussianNB-Model-3(Train)     0.599      0.610   0.597     0.603
6          Decision Tree-Model-4(Test)     0.523      0.530   0.525     0.528
7         Decision Tree-Model-4(Train)     1.000      1.000   1.000     1.000
8      Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531     0.534
9     Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000     1.000
10  Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444     0.497

array([1, 1, 1, 1, 0])

              precision    recall  f1-score   support

           0       0.54      0.66      0.59     39168
           1       0.58      0.45      0.51     40832

    accuracy                           0.55     80000
   macro avg       0.56      0.56      0.55     80000
weighted avg       0.56      0.55      0.55     80000

                                   Name  Accuracy  Precision  Recall  F1 Score
0                   Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1                  Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2                     KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3                    KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4              GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598
5             GaussianNB-Model-3(Train)     0.599      0.610   0.597     0.603
6           Decision Tree-Model-4(Test)     0.523      0.530   0.525     0.528
7          Decision Tree-Model-4(Train)     1.000      1.000   1.000     1.000
8       Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531     0.534
9      Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000     1.000
10   Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444     0.497
11  Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452     0.508

array([[0.50520472, 0.49479528],
       [0.49824679, 0.50175321],
       [0.49824679, 0.50175321],
       [0.52053807, 0.47946193],
       [0.52053807, 0.47946193]])

array([0.49479528, 0.50175321, 0.50175321, ..., 0.50175321, 0.47946193,
       0.47012328])

RandomForestClassifier(random_state=42)

RandomForestClassifier(random_state=42)

array([0, 0, 1, 0, 0])

              precision    recall  f1-score   support

           0       0.58      0.59      0.59      9862
           1       0.60      0.59      0.59     10138

    accuracy                           0.59     20000
   macro avg       0.59      0.59      0.59     20000
weighted avg       0.59      0.59      0.59     20000

                                   Name  Accuracy  Precision  Recall  F1 Score
0                   Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1                  Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2                     KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3                    KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4              GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598
5             GaussianNB-Model-3(Train)     0.599      0.610   0.597     0.603
6           Decision Tree-Model-4(Test)     0.523      0.530   0.525     0.528
7          Decision Tree-Model-4(Train)     1.000      1.000   1.000     1.000
8       Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531     0.534
9      Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000     1.000
10   Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444     0.497
11  Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452     0.508
12          Random Forest-Model-7(Test)     0.589      0.596   0.588     0.592

array([1, 1, 0, 1, 1])

              precision    recall  f1-score   support

           0       1.00      1.00      1.00     39168
           1       1.00      1.00      1.00     40832

    accuracy                           1.00     80000
   macro avg       1.00      1.00      1.00     80000
weighted avg       1.00      1.00      1.00     80000

                                   Name  Accuracy  Precision  Recall  F1 Score
0                   Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1                  Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2                     KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3                    KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4              GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598
5             GaussianNB-Model-3(Train)     0.599      0.610   0.597     0.603
6           Decision Tree-Model-4(Test)     0.523      0.530   0.525     0.528
7          Decision Tree-Model-4(Train)     1.000      1.000   1.000     1.000
8       Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531     0.534
9      Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000     1.000
10   Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444     0.497
11  Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452     0.508
12          Random Forest-Model-7(Test)     0.589      0.596   0.588     0.592
13         Random Forest-Model-7(Train)     1.000      1.000   1.000     1.000

array([[0.58, 0.42],
       [0.72, 0.28],
       [0.38, 0.62],
       [0.67, 0.33],
       [0.63, 0.37]])

array([0.42, 0.28, 0.62, ..., 0.53, 0.38, 0.52])

GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42), n_jobs=-1,
             param_grid=[{'criterion': ['entropy'], 'max_depth': [10, 12],
                          'max_features': ['sqrt', 'log2'],
                          'max_leaf_nodes': [9, 11], 'min_samples_leaf': [5, 7],
                          'min_samples_split': [2, 4], 'n_estimators': [100]}])

GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42), n_jobs=-1,
             param_grid=[{'criterion': ['entropy'], 'max_depth': [10, 12],
                          'max_features': ['sqrt', 'log2'],
                          'max_leaf_nodes': [9, 11], 'min_samples_leaf': [5, 7],
                          'min_samples_split': [2, 4], 'n_estimators': [100]}])

RandomForestClassifier(criterion='entropy', max_depth=10, max_leaf_nodes=11,
                       min_samples_leaf=5, random_state=42)

RandomForestClassifier(criterion='entropy', max_depth=10, max_leaf_nodes=11,
                       min_samples_leaf=5, random_state=42)

{'criterion': 'entropy',
 'max_depth': 10,
 'max_features': 'sqrt',
 'max_leaf_nodes': 11,
 'min_samples_leaf': 5,
 'min_samples_split': 2,
 'n_estimators': 100}

array([1, 0, 1, 0, 0])

              precision    recall  f1-score   support

           0       0.59      0.56      0.58      9862
           1       0.60      0.63      0.61     10138

    accuracy                           0.59     20000
   macro avg       0.59      0.59      0.59     20000
weighted avg       0.59      0.59      0.59     20000

                                   Name  Accuracy  Precision  Recall  F1 Score
0                   Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1                  Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2                     KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3                    KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4              GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598
5             GaussianNB-Model-3(Train)     0.599      0.610   0.597     0.603
6           Decision Tree-Model-4(Test)     0.523      0.530   0.525     0.528
7          Decision Tree-Model-4(Train)     1.000      1.000   1.000     1.000
8       Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531     0.534
9      Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000     1.000
10   Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444     0.497
11  Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452     0.508
12          Random Forest-Model-7(Test)     0.589      0.596   0.588     0.592
13         Random Forest-Model-7(Train)     1.000      1.000   1.000     1.000
14   Random Forest(Tuned)-Model-8(Test)     0.595      0.595   0.626     0.611

array([1, 1, 0, 1, 1])

              precision    recall  f1-score   support

           0       0.59      0.56      0.58     39168
           1       0.60      0.63      0.62     40832

    accuracy                           0.60     80000
   macro avg       0.60      0.60      0.60     80000
weighted avg       0.60      0.60      0.60     80000

                                   Name  Accuracy  Precision  Recall  F1 Score
0                   Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1                  Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2                     KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3                    KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4              GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598
5             GaussianNB-Model-3(Train)     0.599      0.610   0.597     0.603
6           Decision Tree-Model-4(Test)     0.523      0.530   0.525     0.528
7          Decision Tree-Model-4(Train)     1.000      1.000   1.000     1.000
8       Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531     0.534
9      Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000     1.000
10   Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444     0.497
11  Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452     0.508
12          Random Forest-Model-7(Test)     0.589      0.596   0.588     0.592
13         Random Forest-Model-7(Train)     1.000      1.000   1.000     1.000
14   Random Forest(Tuned)-Model-8(Test)     0.595      0.595   0.626     0.611
15  Random Forest(Tuned)-Model-8(Train)     0.598      0.601   0.632     0.616

array([[0.49552448, 0.50447552],
       [0.58604652, 0.41395348],
       [0.440965  , 0.559035  ],
       [0.56627823, 0.43372177],
       [0.59255747, 0.40744253]])

array([0.50447552, 0.41395348, 0.559035  , ..., 0.48969224, 0.4456162 ,
       0.52082183])

AdaBoostClassifier(random_state=42)

AdaBoostClassifier(random_state=42)

array([0, 0, 1, 0, 0])

              precision    recall  f1-score   support

           0       0.60      0.58      0.59      9862
           1       0.60      0.62      0.61     10138

    accuracy                           0.60     20000
   macro avg       0.60      0.60      0.60     20000
weighted avg       0.60      0.60      0.60     20000

                                   Name  Accuracy  Precision  Recall  F1 Score
0                   Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1                  Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2                     KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3                    KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4              GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598
5             GaussianNB-Model-3(Train)     0.599      0.610   0.597     0.603
6           Decision Tree-Model-4(Test)     0.523      0.530   0.525     0.528
7          Decision Tree-Model-4(Train)     1.000      1.000   1.000     1.000
8       Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531     0.534
9      Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000     1.000
10   Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444     0.497
11  Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452     0.508
12          Random Forest-Model-7(Test)     0.589      0.596   0.588     0.592
13         Random Forest-Model-7(Train)     1.000      1.000   1.000     1.000
14   Random Forest(Tuned)-Model-8(Test)     0.595      0.595   0.626     0.611
15  Random Forest(Tuned)-Model-8(Train)     0.598      0.601   0.632     0.616
16     AdaBoostClassifier-Model-9(Test)     0.601      0.604   0.618     0.611

array([1, 1, 0, 1, 1])

              precision    recall  f1-score   support

           0       0.60      0.59      0.59     39168
           1       0.61      0.62      0.62     40832

    accuracy                           0.61     80000
   macro avg       0.61      0.61      0.61     80000
weighted avg       0.61      0.61      0.61     80000

                                   Name  Accuracy  Precision  Recall  F1 Score
0                   Logit-Model-1(Test)     0.602      0.604   0.620     0.612
1                  Logit-Model-1(Train)     0.608      0.614   0.629     0.621
2                     KNN-Model-2(Test)     0.540      0.545   0.559     0.552
3                    KNN-Model-2(Train)     0.709      0.714   0.718     0.716
4              GaussianNB-Model-3(Test)     0.598      0.606   0.591     0.598
5             GaussianNB-Model-3(Train)     0.599      0.610   0.597     0.603
6           Decision Tree-Model-4(Test)     0.523      0.530   0.525     0.528
7          Decision Tree-Model-4(Train)     1.000      1.000   1.000     1.000
8       Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531     0.534
9      Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000     1.000
10   Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444     0.497
11  Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452     0.508
12          Random Forest-Model-7(Test)     0.589      0.596   0.588     0.592
13         Random Forest-Model-7(Train)     1.000      1.000   1.000     1.000
14   Random Forest(Tuned)-Model-8(Test)     0.595      0.595   0.626     0.611
15  Random Forest(Tuned)-Model-8(Train)     0.598      0.601   0.632     0.616
16     AdaBoostClassifier-Model-9(Test)     0.601      0.604   0.618     0.611
17    AdaBoostClassifier-Model-9(Train)     0.606      0.612   0.622     0.617

array([[0.58217837, 0.41782163],
       [0.62052135, 0.37947865],
       [0.41944189, 0.58055811],
       [0.60542716, 0.39457284],
       [0.64281061, 0.35718939]])

array([0.41782163, 0.37947865, 0.58055811, ..., 0.48325558, 0.45416274,
       0.51554871])

{'learning_rate': 0.1, 'n_estimators': 200}

AdaBoostClassifier(learning_rate=0.1, n_estimators=200, random_state=42)

AdaBoostClassifier(learning_rate=0.1, n_estimators=200, random_state=42)

array([0, 0, 1, 0, 0])

              precision    recall  f1-score   support

           0       0.60      0.58      0.59      9862
           1       0.60      0.61      0.61     10138

    accuracy                           0.60     20000
   macro avg       0.60      0.60      0.60     20000
weighted avg       0.60      0.60      0.60     20000

                                        Name  Accuracy  Precision  Recall  \
0                        Logit-Model-1(Test)     0.602      0.604   0.620   
1                       Logit-Model-1(Train)     0.608      0.614   0.629   
2                          KNN-Model-2(Test)     0.540      0.545   0.559   
3                         KNN-Model-2(Train)     0.709      0.714   0.718   
4                   GaussianNB-Model-3(Test)     0.598      0.606   0.591   
5                  GaussianNB-Model-3(Train)     0.599      0.610   0.597   
6                Decision Tree-Model-4(Test)     0.523      0.530   0.525   
7               Decision Tree-Model-4(Train)     1.000      1.000   1.000   
8            Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531   
9           Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000   
10        Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444   
11       Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452   
12               Random Forest-Model-7(Test)     0.589      0.596   0.588   
13              Random Forest-Model-7(Train)     1.000      1.000   1.000   
14        Random Forest(Tuned)-Model-8(Test)     0.595      0.595   0.626   
15       Random Forest(Tuned)-Model-8(Train)     0.598      0.601   0.632   
16          AdaBoostClassifier-Model-9(Test)     0.601      0.604   0.618   
17         AdaBoostClassifier-Model-9(Train)     0.606      0.612   0.622   
18  AdaBoostClassifier(Tuned)-Model-10(Test)     0.600      0.603   0.615   

    F1 Score  
0      0.612  
1      0.621  
2      0.552  
3      0.716  
4      0.598  
5      0.603  
6      0.528  
7      1.000  
8      0.534  
9      1.000  
10     0.497  
11     0.508  
12     0.592  
13     1.000  
14     0.611  
15     0.616  
16     0.611  
17     0.617  
18     0.609

array([1, 1, 0, 1, 1])

              precision    recall  f1-score   support

           0       0.60      0.59      0.59     39168
           1       0.61      0.62      0.62     40832

    accuracy                           0.61     80000
   macro avg       0.61      0.61      0.61     80000
weighted avg       0.61      0.61      0.61     80000

                                         Name  Accuracy  Precision  Recall  \
0                         Logit-Model-1(Test)     0.602      0.604   0.620   
1                        Logit-Model-1(Train)     0.608      0.614   0.629   
2                           KNN-Model-2(Test)     0.540      0.545   0.559   
3                          KNN-Model-2(Train)     0.709      0.714   0.718   
4                    GaussianNB-Model-3(Test)     0.598      0.606   0.591   
5                   GaussianNB-Model-3(Train)     0.599      0.610   0.597   
6                 Decision Tree-Model-4(Test)     0.523      0.530   0.525   
7                Decision Tree-Model-4(Train)     1.000      1.000   1.000   
8             Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531   
9            Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000   
10         Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444   
11        Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452   
12                Random Forest-Model-7(Test)     0.589      0.596   0.588   
13               Random Forest-Model-7(Train)     1.000      1.000   1.000   
14         Random Forest(Tuned)-Model-8(Test)     0.595      0.595   0.626   
15        Random Forest(Tuned)-Model-8(Train)     0.598      0.601   0.632   
16           AdaBoostClassifier-Model-9(Test)     0.601      0.604   0.618   
17          AdaBoostClassifier-Model-9(Train)     0.606      0.612   0.622   
18   AdaBoostClassifier(Tuned)-Model-10(Test)     0.600      0.603   0.615   
19  AdaBoostClassifier(Tuned)-Model-10(Train)     0.607      0.613   0.624   

    F1 Score  
0      0.612  
1      0.621  
2      0.552  
3      0.716  
4      0.598  
5      0.603  
6      0.528  
7      1.000  
8      0.534  
9      1.000  
10     0.497  
11     0.508  
12     0.592  
13     1.000  
14     0.611  
15     0.616  
16     0.611  
17     0.617  
18     0.609  
19     0.618

array([[0.56387764, 0.43612236],
       [0.6892872 , 0.3107128 ],
       [0.34625045, 0.65374955],
       [0.69268473, 0.30731527],
       [0.70780048, 0.29219952]])

array([0.43612236, 0.3107128 , 0.65374955, ..., 0.45255592, 0.42041945,
       0.50738417])

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              feature_weights=None, gamma=None, grow_policy=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_bin=None, max_cat_threshold=None,
              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
              max_leaves=None, min_child_weight=None, missing=nan,
              monotone_constraints=None, multi_strategy=None, n_estimators=None,
              n_jobs=None, num_parallel_tree=None, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              feature_weights=None, gamma=None, grow_policy=None,
              importance_type=None, interaction_constraints=None,
              learning_rate=None, max_bin=None, max_cat_threshold=None,
              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
              max_leaves=None, min_child_weight=None, missing=nan,
              monotone_constraints=None, multi_strategy=None, n_estimators=None,
              n_jobs=None, num_parallel_tree=None, ...)

array([0, 0, 1, 0, 1])

              precision    recall  f1-score   support

           0       0.58      0.57      0.57      9862
           1       0.59      0.60      0.59     10138

    accuracy                           0.58     20000
   macro avg       0.58      0.58      0.58     20000
weighted avg       0.58      0.58      0.58     20000

                                         Name  Accuracy  Precision  Recall  \
0                         Logit-Model-1(Test)     0.602      0.604   0.620   
1                        Logit-Model-1(Train)     0.608      0.614   0.629   
2                           KNN-Model-2(Test)     0.540      0.545   0.559   
3                          KNN-Model-2(Train)     0.709      0.714   0.718   
4                    GaussianNB-Model-3(Test)     0.598      0.606   0.591   
5                   GaussianNB-Model-3(Train)     0.599      0.610   0.597   
6                 Decision Tree-Model-4(Test)     0.523      0.530   0.525   
7                Decision Tree-Model-4(Train)     1.000      1.000   1.000   
8             Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531   
9            Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000   
10         Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444   
11        Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452   
12                Random Forest-Model-7(Test)     0.589      0.596   0.588   
13               Random Forest-Model-7(Train)     1.000      1.000   1.000   
14         Random Forest(Tuned)-Model-8(Test)     0.595      0.595   0.626   
15        Random Forest(Tuned)-Model-8(Train)     0.598      0.601   0.632   
16           AdaBoostClassifier-Model-9(Test)     0.601      0.604   0.618   
17          AdaBoostClassifier-Model-9(Train)     0.606      0.612   0.622   
18   AdaBoostClassifier(Tuned)-Model-10(Test)     0.600      0.603   0.615   
19  AdaBoostClassifier(Tuned)-Model-10(Train)     0.607      0.613   0.624   
20               XGBClassifier-Model-11(Test)     0.583      0.588   0.598   

    F1 Score  
0      0.612  
1      0.621  
2      0.552  
3      0.716  
4      0.598  
5      0.603  
6      0.528  
7      1.000  
8      0.534  
9      1.000  
10     0.497  
11     0.508  
12     0.592  
13     1.000  
14     0.611  
15     0.616  
16     0.611  
17     0.617  
18     0.609  
19     0.618  
20     0.593

array([1, 1, 0, 1, 1])

              precision    recall  f1-score   support

           0       0.71      0.70      0.70     39168
           1       0.71      0.73      0.72     40832

    accuracy                           0.71     80000
   macro avg       0.71      0.71      0.71     80000
weighted avg       0.71      0.71      0.71     80000

                                         Name  Accuracy  Precision  Recall  \
0                         Logit-Model-1(Test)     0.602      0.604   0.620   
1                        Logit-Model-1(Train)     0.608      0.614   0.629   
2                           KNN-Model-2(Test)     0.540      0.545   0.559   
3                          KNN-Model-2(Train)     0.709      0.714   0.718   
4                    GaussianNB-Model-3(Test)     0.598      0.606   0.591   
5                   GaussianNB-Model-3(Train)     0.599      0.610   0.597   
6                 Decision Tree-Model-4(Test)     0.523      0.530   0.525   
7                Decision Tree-Model-4(Train)     1.000      1.000   1.000   
8             Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531   
9            Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000   
10         Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444   
11        Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452   
12                Random Forest-Model-7(Test)     0.589      0.596   0.588   
13               Random Forest-Model-7(Train)     1.000      1.000   1.000   
14         Random Forest(Tuned)-Model-8(Test)     0.595      0.595   0.626   
15        Random Forest(Tuned)-Model-8(Train)     0.598      0.601   0.632   
16           AdaBoostClassifier-Model-9(Test)     0.601      0.604   0.618   
17          AdaBoostClassifier-Model-9(Train)     0.606      0.612   0.622   
18   AdaBoostClassifier(Tuned)-Model-10(Test)     0.600      0.603   0.615   
19  AdaBoostClassifier(Tuned)-Model-10(Train)     0.607      0.613   0.624   
20               XGBClassifier-Model-11(Test)     0.583      0.588   0.598   
21              XGBClassifier-Model-11(Train)     0.713      0.715   0.730   

    F1 Score  
0      0.612  
1      0.621  
2      0.552  
3      0.716  
4      0.598  
5      0.603  
6      0.528  
7      1.000  
8      0.534  
9      1.000  
10     0.497  
11     0.508  
12     0.592  
13     1.000  
14     0.611  
15     0.616  
16     0.611  
17     0.617  
18     0.609  
19     0.618  
20     0.593  
21     0.722

array([[0.6249052 , 0.3750948 ],
       [0.7202593 , 0.2797407 ],
       [0.29613912, 0.7038609 ],
       [0.5834572 , 0.41654283],
       [0.46869588, 0.5313041 ]], dtype=float32)

array([0.3750948, 0.2797407, 0.7038609, ..., 0.5532377, 0.5157955,
       0.397631 ], dtype=float32)

Fitting 5 folds for each of 32 candidates, totalling 160 fits

GridSearchCV(cv=5,
             estimator=XGBClassifier(base_score=None, booster=None,
                                     callbacks=None, colsample_bylevel=None,
                                     colsample_bynode=None,
                                     colsample_bytree=None, device=None,
                                     early_stopping_rounds=None,
                                     enable_categorical=False,
                                     eval_metric='logloss', feature_types=None,
                                     feature_weights=None, gamma=None,
                                     grow_policy=None, importance_type=None,
                                     interaction_constraint...
                                     max_delta_step=None, max_depth=None,
                                     max_leaves=None, min_child_weight=None,
                                     missing=nan, monotone_constraints=None,
                                     multi_strategy=None, n_estimators=None,
                                     n_jobs=None, num_parallel_tree=None, ...),
             n_jobs=-1,
             param_grid={'colsample_bytree': [0.8, 1.0],
                         'learning_rate': [0.05, 0.1], 'max_depth': [3, 5],
                         'n_estimators': [100, 200], 'subsample': [0.8, 1.0]},
             scoring='accuracy', verbose=2)

GridSearchCV(cv=5,
             estimator=XGBClassifier(base_score=None, booster=None,
                                     callbacks=None, colsample_bylevel=None,
                                     colsample_bynode=None,
                                     colsample_bytree=None, device=None,
                                     early_stopping_rounds=None,
                                     enable_categorical=False,
                                     eval_metric='logloss', feature_types=None,
                                     feature_weights=None, gamma=None,
                                     grow_policy=None, importance_type=None,
                                     interaction_constraint...
                                     max_delta_step=None, max_depth=None,
                                     max_leaves=None, min_child_weight=None,
                                     missing=nan, monotone_constraints=None,
                                     multi_strategy=None, n_estimators=None,
                                     n_jobs=None, num_parallel_tree=None, ...),
             n_jobs=-1,
             param_grid={'colsample_bytree': [0.8, 1.0],
                         'learning_rate': [0.05, 0.1], 'max_depth': [3, 5],
                         'n_estimators': [100, 200], 'subsample': [0.8, 1.0]},
             scoring='accuracy', verbose=2)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=0.8, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric='logloss',
              feature_types=None, feature_weights=None, gamma=None,
              grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=0.1, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=3, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=100, n_jobs=None,
              num_parallel_tree=None, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=0.8, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric='logloss',
              feature_types=None, feature_weights=None, gamma=None,
              grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=0.1, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=3, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=100, n_jobs=None,
              num_parallel_tree=None, ...)

{'colsample_bytree': 0.8,
 'learning_rate': 0.1,
 'max_depth': 3,
 'n_estimators': 100,
 'subsample': 1.0}

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=0.8, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric='logloss',
              feature_types=None, feature_weights=None, gamma=None,
              grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=0.1, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=3, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=100, n_jobs=None,
              num_parallel_tree=None, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=0.8, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric='logloss',
              feature_types=None, feature_weights=None, gamma=None,
              grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=0.1, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=3, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=100, n_jobs=None,
              num_parallel_tree=None, ...)

array([0, 0, 1, 0, 0])

              precision    recall  f1-score   support

           0       0.60      0.59      0.59      9862
           1       0.60      0.61      0.61     10138

    accuracy                           0.60     20000
   macro avg       0.60      0.60      0.60     20000
weighted avg       0.60      0.60      0.60     20000

                                         Name  Accuracy  Precision  Recall  \
0                         Logit-Model-1(Test)     0.602      0.604   0.620   
1                        Logit-Model-1(Train)     0.608      0.614   0.629   
2                           KNN-Model-2(Test)     0.540      0.545   0.559   
3                          KNN-Model-2(Train)     0.709      0.714   0.718   
4                    GaussianNB-Model-3(Test)     0.598      0.606   0.591   
5                   GaussianNB-Model-3(Train)     0.599      0.610   0.597   
6                 Decision Tree-Model-4(Test)     0.523      0.530   0.525   
7                Decision Tree-Model-4(Train)     1.000      1.000   1.000   
8             Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531   
9            Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000   
10         Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444   
11        Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452   
12                Random Forest-Model-7(Test)     0.589      0.596   0.588   
13               Random Forest-Model-7(Train)     1.000      1.000   1.000   
14         Random Forest(Tuned)-Model-8(Test)     0.595      0.595   0.626   
15        Random Forest(Tuned)-Model-8(Train)     0.598      0.601   0.632   
16           AdaBoostClassifier-Model-9(Test)     0.601      0.604   0.618   
17          AdaBoostClassifier-Model-9(Train)     0.606      0.612   0.622   
18   AdaBoostClassifier(Tuned)-Model-10(Test)     0.600      0.603   0.615   
19  AdaBoostClassifier(Tuned)-Model-10(Train)     0.607      0.613   0.624   
20               XGBClassifier-Model-11(Test)     0.583      0.588   0.598   
21              XGBClassifier-Model-11(Train)     0.713      0.715   0.730   
22        XGBClassifier(Tuned)-Model-12(Test)     0.601      0.605   0.613   

    F1 Score  
0      0.612  
1      0.621  
2      0.552  
3      0.716  
4      0.598  
5      0.603  
6      0.528  
7      1.000  
8      0.534  
9      1.000  
10     0.497  
11     0.508  
12     0.592  
13     1.000  
14     0.611  
15     0.616  
16     0.611  
17     0.617  
18     0.609  
19     0.618  
20     0.593  
21     0.722  
22     0.609

array([1, 1, 0, 1, 1])

              precision    recall  f1-score   support

           0       0.61      0.60      0.60     39168
           1       0.62      0.63      0.62     40832

    accuracy                           0.61     80000
   macro avg       0.61      0.61      0.61     80000
weighted avg       0.61      0.61      0.61     80000

                                         Name  Accuracy  Precision  Recall  \
0                         Logit-Model-1(Test)     0.602      0.604   0.620   
1                        Logit-Model-1(Train)     0.608      0.614   0.629   
2                           KNN-Model-2(Test)     0.540      0.545   0.559   
3                          KNN-Model-2(Train)     0.709      0.714   0.718   
4                    GaussianNB-Model-3(Test)     0.598      0.606   0.591   
5                   GaussianNB-Model-3(Train)     0.599      0.610   0.597   
6                 Decision Tree-Model-4(Test)     0.523      0.530   0.525   
7                Decision Tree-Model-4(Train)     1.000      1.000   1.000   
8             Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531   
9            Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000   
10         Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444   
11        Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452   
12                Random Forest-Model-7(Test)     0.589      0.596   0.588   
13               Random Forest-Model-7(Train)     1.000      1.000   1.000   
14         Random Forest(Tuned)-Model-8(Test)     0.595      0.595   0.626   
15        Random Forest(Tuned)-Model-8(Train)     0.598      0.601   0.632   
16           AdaBoostClassifier-Model-9(Test)     0.601      0.604   0.618   
17          AdaBoostClassifier-Model-9(Train)     0.606      0.612   0.622   
18   AdaBoostClassifier(Tuned)-Model-10(Test)     0.600      0.603   0.615   
19  AdaBoostClassifier(Tuned)-Model-10(Train)     0.607      0.613   0.624   
20               XGBClassifier-Model-11(Test)     0.583      0.588   0.598   
21              XGBClassifier-Model-11(Train)     0.713      0.715   0.730   
22        XGBClassifier(Tuned)-Model-12(Test)     0.601      0.605   0.613   
23       XGBClassifier(Tuned)-Model-12(Train)     0.613      0.619   0.628   

    F1 Score  
0      0.612  
1      0.621  
2      0.552  
3      0.716  
4      0.598  
5      0.603  
6      0.528  
7      1.000  
8      0.534  
9      1.000  
10     0.497  
11     0.508  
12     0.592  
13     1.000  
14     0.611  
15     0.616  
16     0.611  
17     0.617  
18     0.609  
19     0.618  
20     0.593  
21     0.722  
22     0.609  
23     0.623

array([[0.55821514, 0.4417849 ],
       [0.6628357 , 0.3371643 ],
       [0.3925438 , 0.6074562 ],
       [0.6607305 , 0.33926955],
       [0.6040486 , 0.39595136]], dtype=float32)

array([0.4417849 , 0.3371643 , 0.6074562 , ..., 0.5285576 , 0.38694397,
       0.5013471 ], dtype=float32)

Optimization terminated successfully.
         Current function value: 0.657397
         Iterations 5
                           Logit Regression Results                           
==============================================================================
Dep. Variable:              Completed   No. Observations:                80000
Model:                          Logit   Df Residuals:                    79965
Method:                           MLE   Df Model:                           34
Date:                Mon, 05 Jan 2026   Pseudo R-squ.:                 0.05128
Time:                        11:21:35   Log-Likelihood:                -52592.
converged:                       True   LL-Null:                       -55434.
Covariance Type:            nonrobust   LLR p-value:                     0.000
================================================================================================
                                   coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------------------------
Gender                           0.0272      0.014      2.007      0.045       0.001       0.054
Age                              0.0067      0.011      0.609      0.542      -0.015       0.028
Education_Level                  0.0083      0.008      1.059      0.290      -0.007       0.024
Employment_Status                0.0093      0.007      1.311      0.190      -0.005       0.023
City                             0.0015      0.002      0.918      0.359      -0.002       0.005
Device_Type                     -0.0018      0.013     -0.139      0.889      -0.027       0.024
Internet_Connection_Quality      0.0094      0.011      0.867      0.386      -0.012       0.031
Course_Name                     -0.0020      0.004     -0.548      0.584      -0.009       0.005
Category                         0.0065      0.010      0.678      0.498      -0.012       0.025
Course_Level                     0.0355      0.026      1.371      0.170      -0.015       0.086
Course_Duration_Days             0.0927      0.032      2.858      0.004       0.029       0.156
Instructor_Rating               -0.0092      0.019     -0.482      0.629      -0.046       0.028
Login_Frequency                  0.0132      0.013      1.053      0.292      -0.011       0.038
Average_Session_Duration_Min    -0.0053      0.010     -0.520      0.603      -0.025       0.015
Video_Completion_Rate           -0.2981      0.045     -6.598      0.000      -0.387      -0.210
Discussion_Participation        -0.0045      0.010     -0.463      0.643      -0.024       0.015
Time_Spent_Hours                -0.2709      0.012    -23.544      0.000      -0.293      -0.248
Days_Since_Last_Login            0.0765      0.009      8.950      0.000       0.060       0.093
Notifications_Checked           -0.0030      0.009     -0.323      0.747      -0.021       0.015
Peer_Interaction_Score          -0.0100      0.011     -0.955      0.340      -0.031       0.011
Assignments_Submitted           -0.0191      0.034     -0.566      0.571      -0.085       0.047
Assignments_Missed               0.0374      0.045      0.838      0.402      -0.050       0.125
Quiz_Attempts                    0.0244      0.011      2.201      0.028       0.003       0.046
Quiz_Score_Avg                  -0.1569      0.010    -15.026      0.000      -0.177      -0.136
Project_Grade                    0.0043      0.011      0.380      0.704      -0.018       0.026
Progress_Percentage             -0.3835      0.074     -5.171      0.000      -0.529      -0.238
Rewatch_Count                   -0.0083      0.010     -0.826      0.409      -0.028       0.011
Payment_Mode                    -0.0016      0.004     -0.419      0.676      -0.009       0.006
Fee_Paid                        -0.1507      0.043     -3.505      0.000      -0.235      -0.066
Discount_Used                   -0.0374      0.019     -1.957      0.050      -0.075    6.59e-05
Payment_Amount                  -0.2184      0.035     -6.159      0.000      -0.288      -0.149
App_Usage_Percentage            -0.0284      0.010     -2.726      0.006      -0.049      -0.008
Reminder_Emails_Clicked          0.0091      0.009      0.966      0.334      -0.009       0.028
Support_Tickets_Raised          -0.0048      0.008     -0.622      0.534      -0.020       0.010
Satisfaction_Rating              0.0177      0.012      1.505      0.132      -0.005       0.041
================================================================================================

75721    0.421062
80184    0.324220
19864    0.637756
76699    0.310405
92991    0.313945
dtype: float64

[0, 0, 1, 0, 0]

              precision    recall  f1-score   support

           0       0.60      0.58      0.59      9862
           1       0.61      0.62      0.61     10138

    accuracy                           0.60     20000
   macro avg       0.60      0.60      0.60     20000
weighted avg       0.60      0.60      0.60     20000

                                         Name  Accuracy  Precision  Recall  \
0                         Logit-Model-1(Test)     0.602      0.604   0.620   
1                        Logit-Model-1(Train)     0.608      0.614   0.629   
2                           KNN-Model-2(Test)     0.540      0.545   0.559   
3                          KNN-Model-2(Train)     0.709      0.714   0.718   
4                    GaussianNB-Model-3(Test)     0.598      0.606   0.591   
5                   GaussianNB-Model-3(Train)     0.599      0.610   0.597   
6                 Decision Tree-Model-4(Test)     0.523      0.530   0.525   
7                Decision Tree-Model-4(Train)     1.000      1.000   1.000   
8             Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531   
9            Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000   
10         Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444   
11        Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452   
12                Random Forest-Model-7(Test)     0.589      0.596   0.588   
13               Random Forest-Model-7(Train)     1.000      1.000   1.000   
14         Random Forest(Tuned)-Model-8(Test)     0.595      0.595   0.626   
15        Random Forest(Tuned)-Model-8(Train)     0.598      0.601   0.632   
16           AdaBoostClassifier-Model-9(Test)     0.601      0.604   0.618   
17          AdaBoostClassifier-Model-9(Train)     0.606      0.612   0.622   
18   AdaBoostClassifier(Tuned)-Model-10(Test)     0.600      0.603   0.615   
19  AdaBoostClassifier(Tuned)-Model-10(Train)     0.607      0.613   0.624   
20               XGBClassifier-Model-11(Test)     0.583      0.588   0.598   
21              XGBClassifier-Model-11(Train)     0.713      0.715   0.730   
22        XGBClassifier(Tuned)-Model-12(Test)     0.601      0.605   0.613   
23       XGBClassifier(Tuned)-Model-12(Train)     0.613      0.619   0.628   
24                         PCA-Logit-Model-13     0.602      0.605   0.619   

    F1 Score  
0      0.612  
1      0.621  
2      0.552  
3      0.716  
4      0.598  
5      0.603  
6      0.528  
7      1.000  
8      0.534  
9      1.000  
10     0.497  
11     0.508  
12     0.592  
13     1.000  
14     0.611  
15     0.616  
16     0.611  
17     0.617  
18     0.609  
19     0.618  
20     0.593  
21     0.722  
22     0.609  
23     0.623  
24     0.612

DecisionTreeClassifier(random_state=42)

DecisionTreeClassifier(random_state=42)

array([1, 0, 1, 0, 0])

              precision    recall  f1-score   support

           0       0.52      0.52      0.52      9862
           1       0.53      0.53      0.53     10138

    accuracy                           0.52     20000
   macro avg       0.52      0.52      0.52     20000
weighted avg       0.52      0.52      0.52     20000

                                         Name  Accuracy  Precision  Recall  \
0                         Logit-Model-1(Test)     0.602      0.604   0.620   
1                        Logit-Model-1(Train)     0.608      0.614   0.629   
2                           KNN-Model-2(Test)     0.540      0.545   0.559   
3                          KNN-Model-2(Train)     0.709      0.714   0.718   
4                    GaussianNB-Model-3(Test)     0.598      0.606   0.591   
5                   GaussianNB-Model-3(Train)     0.599      0.610   0.597   
6                 Decision Tree-Model-4(Test)     0.523      0.530   0.525   
7                Decision Tree-Model-4(Train)     1.000      1.000   1.000   
8             Decision Tree(FI)-Model-5(Test)     0.530      0.537   0.531   
9            Decision Tree(FI)-Model-5(Train)     1.000      1.000   1.000   
10         Decision Tree(Tuned)-Model-6(Test)     0.544      0.564   0.444   
11        Decision Tree(Tuned)-Model-6(Train)     0.553      0.580   0.452   
12                Random Forest-Model-7(Test)     0.589      0.596   0.588   
13               Random Forest-Model-7(Train)     1.000      1.000   1.000   
14         Random Forest(Tuned)-Model-8(Test)     0.595      0.595   0.626   
15        Random Forest(Tuned)-Model-8(Train)     0.598      0.601   0.632   
16           AdaBoostClassifier-Model-9(Test)     0.601      0.604   0.618   
17          AdaBoostClassifier-Model-9(Train)     0.606      0.612   0.622   
18   AdaBoostClassifier(Tuned)-Model-10(Test)     0.600      0.603   0.615   
19  AdaBoostClassifier(Tuned)-Model-10(Train)     0.607      0.613   0.624   
20               XGBClassifier-Model-11(Test)     0.583      0.588   0.598   
21              XGBClassifier-Model-11(Train)     0.713      0.715   0.730   
22        XGBClassifier(Tuned)-Model-12(Test)     0.601      0.605   0.613   
23       XGBClassifier(Tuned)-Model-12(Train)     0.613      0.619   0.628   
24                         PCA-Logit-Model-13     0.602      0.605   0.619   
25                 PCA-Decision Tree-Model-14     0.523      0.530   0.525   

    F1 Score  
0      0.612  
1      0.621  
2      0.552  
3      0.716  
4      0.598  
5      0.603  
6      0.528  
7      1.000  
8      0.534  
9      1.000  
10     0.497  
11     0.508  
12     0.592  
13     1.000  
14     0.611  
15     0.616  
16     0.611  
17     0.617  
18     0.609  
19     0.618  
20     0.593  
21     0.722  
22     0.609  
23     0.623  
24     0.612  
25     0.528

	Gender	Age	Education_Level	Employment_Status	City	Device_Type	Internet_Connection_Quality	Course_Name	Category	Course_Level	Course_Duration_Days	Instructor_Rating	Login_Frequency	Average_Session_Duration_Min	Video_Completion_Rate	Discussion_Participation	Time_Spent_Hours	Days_Since_Last_Login	Notifications_Checked	Peer_Interaction_Score	Assignments_Submitted	Assignments_Missed	Quiz_Attempts	Quiz_Score_Avg	Project_Grade	Progress_Percentage	Rewatch_Count	Payment_Mode	Fee_Paid	Discount_Used	Payment_Amount	App_Usage_Percentage	Reminder_Emails_Clicked	Support_Tickets_Raised	Satisfaction_Rating
0	1	-0.750	3.0	2	6	0	1.0	0	4	1.0	0.500000	0.666667	-0.666667	-0.285714	-0.310345	0.0	-0.385965	-0.375	0.333333	-0.714286	1.5	-2.0	0.333333	0.439306	0.137441	0.976744	-1.0	4	0	0	-0.573628	-0.703704	0.5	3.0	-0.636364
1	0	-1.000	2.0	2	4	0	2.0	4	4	0.0	1.500000	0.333333	-0.333333	0.214286	0.693103	0.0	-0.315789	-0.125	0.000000	0.535714	-0.5	0.5	-0.333333	0.294798	-1.222749	0.093023	0.0	0	1	0	0.706361	0.666667	-1.0	-1.0	0.272727
2	0	1.125	1.0	2	3	1	1.0	5	4	2.0	0.000000	0.333333	0.000000	-1.785714	0.400000	0.5	-0.385965	1.875	0.000000	0.142857	1.5	-1.5	-0.333333	1.543353	0.928910	1.441860	0.0	3	1	0	0.164101	0.629630	-0.5	-1.0	0.727273
3	0	0.500	3.0	0	13	1	0.0	7	1	2.0	-0.166667	-0.333333	-1.000000	-0.500000	-0.024138	-0.5	0.824561	1.875	1.333333	0.035714	-2.5	2.5	0.000000	-0.820809	-0.800948	-1.703488	1.0	5	1	0	0.028173	-0.962963	0.0	2.0	-0.363636
4	0	-0.750	1.0	1	9	0	1.0	4	4	0.0	1.500000	0.333333	-1.000000	0.142857	0.772414	-0.5	-0.385965	0.000	0.666667	0.428571	0.0	0.0	1.333333	0.664740	1.170616	0.633721	1.0	1	1	1	0.514377	0.851852	0.5	-1.0	-0.181818
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
99995	0	1.125	2.0	2	5	1	1.0	1	2	2.0	-0.500000	-0.666667	0.000000	1.000000	0.972414	1.0	0.877193	0.250	0.000000	-0.714286	0.5	-1.0	-0.666667	0.589595	0.274882	1.046512	0.5	5	1	0	-0.124891	1.185185	0.5	1.0	-0.363636
99996	0	-0.125	2.0	1	4	1	0.0	1	2	2.0	-0.500000	-0.666667	0.333333	-0.142857	0.575862	1.5	-0.385965	0.625	-1.000000	0.750000	0.0	0.0	0.666667	-0.086705	1.009479	0.505814	-0.5	5	1	0	-0.139994	0.111111	0.0	-1.0	-1.090909
99997	1	-1.000	1.0	0	0	0	1.0	6	3	1.0	0.166667	-1.000000	-0.333333	-0.071429	0.051724	-1.0	0.543860	1.500	-0.333333	0.357143	0.0	0.0	-0.333333	-0.971098	0.767773	0.180233	0.5	5	1	1	0.030497	-0.259259	0.0	-1.0	-0.090909
99998	0	0.375	2.0	2	0	1	0.0	1	2	2.0	-0.500000	-0.666667	0.333333	-0.785714	-0.844828	-0.5	0.526316	-0.125	0.000000	-0.321429	0.0	0.0	2.000000	-0.248555	0.161137	-0.383721	1.0	0	1	0	-0.106884	0.370370	-0.5	-1.0	0.454545
99999	1	0.500	3.0	2	1	0	2.0	7	1	2.0	-0.166667	-0.333333	0.000000	1.214286	-0.296552	-1.0	0.070175	0.750	1.333333	0.321429	1.5	-1.5	0.000000	-0.578035	0.483412	1.005814	1.0	0	1	0	0.117630	-0.481481	-0.5	-1.0	-0.272727

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18
0	-1.003730	-3.320966	1.234242	1.227996	-2.046160	-0.954942	3.557567	0.279726	-1.167832	-0.542033	-0.108152	0.958137	-0.644089	-0.655836	0.519336	-0.701383	-0.409125	-0.762163	-0.315608
1	-2.994903	0.657030	-2.750106	2.027313	0.273107	0.912761	-0.142815	0.226739	0.753094	-0.947799	0.760818	-0.486261	0.868133	0.751500	-0.563946	-0.210647	0.218324	1.317837	1.168343
2	-4.009643	1.711504	0.240452	0.990107	-2.684743	-0.218804	0.253286	-0.614127	2.193415	0.500803	0.371842	0.241922	0.659093	0.781887	1.504024	0.605035	-1.495029	-0.112257	0.068494
3	5.994561	3.840514	2.244081	-1.774813	4.140742	-0.296129	1.028160	0.415821	-1.031120	2.199874	1.083793	0.687537	0.473026	0.122322	-0.449173	0.496109	1.192114	-0.414756	0.465401
4	1.998731	0.671759	-1.740147	2.009950	-1.108059	0.355780	-1.273395	-0.888741	-0.169095	0.258297	0.233948	0.489905	1.303961	0.811921	-0.540801	1.142155	-0.653207	0.223974	-0.277023

Capstone¶

NULL VALUE TREATMENT¶

Outlier Detection¶

Univariate Analysis¶

Numeric¶

Categorical¶

Bivariate Analysis¶

Numeric vs Numeric¶

Numeric vs Categoric¶

Categoric vs Categoric¶

Target Variable Analysis¶

Stats Test¶

Chi Square test for independence¶

T-Test¶

Encoding¶

Dropping the columns¶

N-1 Dummy¶

Ordinal Encoding¶

Label Encoding¶

Base Model¶

Scaling¶

Base Model - 1 - Logit¶

User def function for evaluation metrics¶

Checking the imbalance of the target variable¶

Model-2-KNN¶

Model-3-GaussianNB¶

Model-4-Decision Tree¶

Feature Importance-DT¶

Model-5-Decision Tree(FI)¶

Model-6-Decision Tree(Tuned)¶

Model-7-Random Forest¶

Model-8-Random Forest(Tuned)¶

Model-9-AdaBoostClassifier¶

Model-10-AdaBoostClassifier(Tuned)¶

Model-11-XGBClassifier¶

Model-12-XGBClassifier(Tuned)¶

PCA¶

	Student_ID	Name	Gender	Age	Education_Level	Employment_Status	City	Device_Type	Internet_Connection_Quality	Course_ID	Course_Name	Category	Course_Level	Course_Duration_Days	Instructor_Rating	Login_Frequency	Average_Session_Duration_Min	Video_Completion_Rate	Discussion_Participation	Time_Spent_Hours	Days_Since_Last_Login	Notifications_Checked	Peer_Interaction_Score	Assignments_Submitted	Assignments_Missed	Quiz_Attempts	Quiz_Score_Avg	Project_Grade	Progress_Percentage	Rewatch_Count	Enrollment_Date	Payment_Mode	Fee_Paid	Discount_Used	Payment_Amount	App_Usage_Percentage	Reminder_Emails_Clicked	Support_Tickets_Raised	Satisfaction_Rating	Completed
0	STU100000	Vihaan Patel	Male	19.0	Diploma	Student	Indore	Laptop	Medium	C102	Data Analysis with Python	Programming	Intermediate	60	4.7	3.0	30	55.0	2.0	0.5	1	6	4.3	8.0	1	5	80.9	71.2	70.8	0.0	01-06-2024	Scholarship	No	No	1740	49.0	3	4	3.5	Completed
1	STU100001	Arjun Nair	Female	17.0	Bachelor	Student	Delhi	Laptop	Low	C106	Machine Learning A-Z	Programming	Advanced	90	4.6	4.0	37	84.1	2.0	0.9	3	5	7.8	4.0	6	3	78.4	42.5	55.6	2.0	27-04-2025	Credit Card	Yes	No	6147	86.0	0	0	4.5	Not Completed
2	STU100002	Aditya Bhardwaj	Female	34.0	Master	Student	Chennai	Mobile	Medium	C101	Python Basics	Programming	Beginner	45	4.6	5.0	9	75.6	3.0	0.5	19	5	6.7	8.0	2	3	100.0	87.9	78.8	2.0	20-01-2024	NetBanking	Yes	No	4280	85.0	1	0	5.0	Completed
3	STU100003	Krishna Singh	Female	29.0	Diploma	Employed	Surat	Mobile	High	C105	UI/UX Design Fundamentals	Design	Beginner	40	4.4	2.0	27	63.3	1.0	7.4	19	9	6.4	0.0	10	4	59.1	51.4	24.7	4.0	13-05-2025	UPI	Yes	No	3812	42.0	2	3	3.8	Completed
4	STU100004	Krishna Nair	Female	19.0	Master	Self-Employed	Lucknow	Laptop	Medium	C106	Machine Learning A-Z	Programming	Advanced	90	4.6	2.0	36	86.4	1.0	0.5	4	7	7.5	5.0	5	8	84.8	93.0	64.9	4.0	19-12-2024	Debit Card	Yes	Yes	5486	91.0	3	0	4.0	Completed

	count	mean	std	min	25%	50%	75%	max
Age	95226.0	25.335948	5.472524	17.0	21.0	25.0	29.0	52.0
Course_Duration_Days	100000.0	51.817300	20.324801	25.0	30.0	45.0	60.0	90.0
Instructor_Rating	100000.0	4.444478	0.202631	4.1	4.3	4.5	4.6	4.7
Login_Frequency	99888.0	4.777030	1.832363	0.0	3.0	5.0	6.0	15.0
Average_Session_Duration_Min	100000.0	33.878180	10.341964	5.0	27.0	34.0	41.0	81.0
Video_Completion_Rate	100000.0	62.174580	19.558126	5.0	48.5	64.0	77.5	99.9
Discussion_Participation	99045.0	2.283619	1.528586	0.0	1.0	2.0	3.0	12.0
Time_Spent_Hours	100000.0	3.873632	3.781185	0.5	0.5	2.7	6.2	25.6
Days_Since_Last_Login	100000.0	6.188860	6.982047	0.0	1.0	4.0	9.0	99.0
Notifications_Checked	100000.0	5.232110	2.401486	0.0	4.0	5.0	7.0	18.0
Peer_Interaction_Score	100000.0	6.294509	1.977552	0.0	4.9	6.3	7.7	10.0
Assignments_Submitted	99048.0	4.734826	1.620023	0.0	4.0	5.0	6.0	10.0
Assignments_Missed	100000.0	5.123450	1.692808	0.0	4.0	5.0	6.0	10.0
Quiz_Attempts	100000.0	3.772330	2.021276	0.0	2.0	4.0	5.0	16.0
Quiz_Score_Avg	100000.0	73.276201	12.552344	19.6	64.7	73.3	82.0	100.0
Project_Grade	100000.0	68.189534	15.312036	0.0	57.7	68.3	78.8	100.0
Progress_Percentage	99665.0	53.827156	12.515789	7.6	45.3	54.0	62.5	98.6
Rewatch_Count	97524.0	2.229492	1.482424	0.0	1.0	2.0	3.0	15.0
Payment_Amount	100000.0	3253.427120	2084.391775	0.0	1242.0	3715.0	4685.0	7149.0
App_Usage_Percentage	99974.0	67.876098	19.113137	0.0	55.0	68.0	82.0	100.0
Reminder_Emails_Clicked	100000.0	2.332650	1.584626	0.0	1.0	2.0	3.0	13.0
Support_Tickets_Raised	100000.0	0.870980	0.951569	0.0	0.0	1.0	1.0	8.0
Satisfaction_Rating	98918.0	4.147066	0.689646	1.0	3.7	4.2	4.8	5.0

Gender	Female	Male	Other
Education_Level
Bachelor	27646	26220	1090
Diploma	5088	4741	203
HighSchool	5028	4880	199
Master	10962	10462	435
PhD	1463	1516	67

Gender	Female	Male	Other
Employment_Status
Employed	22721	21484	886
Self-Employed	2493	2486	88
Student	22560	21451	918
Unemployed	2413	2398	102

Education_Level	Bachelor	Diploma	HighSchool	Master	PhD
Employment_Status
Employed	24812	4537	4466	9854	1422
Self-Employed	2834	470	533	1119	111
Student	24605	4560	4595	9803	1366
Unemployed	2705	465	513	1083	147

Course_Name	Data Analysis with Python	Digital Marketing Essentials	Excel for Business	Introduction to AI	Machine Learning A-Z	Python Basics	Statistics for Data Science	UI/UX Design Fundamentals
Completed
Completed	7553	6175	6206	6242	5509	8261	4932	4152
Not Completed	7912	6511	6425	6460	5689	8546	5093	4334

Discount_Used	No	Yes
Fee_Paid
No	23948	5902
Yes	56110	14040